Well, I think he's, you know, he's always had a strong arm. He's very elusive in the pocket. You know, he's a good athlete. He can run, play against him a couple of different times.  He can spin the ball. So, but I just, I mean, he's a good quarterback. He's an NFL quarterback. He's an NFL quarterback. He's a good quarterback. It doesn't really matter. I know what everybody wants to say while he's not Wilson, well, he's got a pretty good group of guys around him. He's got really, really talented running backs. He's got great wide receivers. He's a good quarterback and he's proven that in this league. I have no idea what that means. Ask Coach Pete. Yeah, sure. Yeah, I'm sure he is. He's also said we need to run the ball better. If I notice both of those quotes. So, which quote is true? I don't know. Really wasn't, it really wasn't a rotation. It really wasn't. When they went up 28, whatever it was, it was 3 or 10, whatever it was, I just turned to Coach Oak and said, why don't you put Dean in? It's 28 to 3. At some point in time, you're going to need all these guys during the season, right? So, the more, and he's played quite a bit, and but he hadn't played in our system as much. So, we just put him in. It wasn't, it had nothing to do with Hawk. Had nothing to do with any of those guys other than it was 28 to 3. And I felt like, okay, let's play the other guys. There's not like, I think there's a big drop off in any of those guys. I don't think there is. So, it really didn't matter to me. It wasn't, it was not taking a guy out of the game for any reason. It was just putting the other guy in to give him some playing time. Yeah, it felt like in that game, there was a lot of stuff we were doing, and it was a little more complicated coverage-wise. And I just felt like, again, it was not anything to do with Michael Ford. It had more to do with just Eric's experience as a safety and as a defensive back in a game like that that I thought could really help us. Well, no, I think, you know, you, I think you have a hard time as a coach trying to foster relationships between players. The thing of what you can foster is bringing guys back into the program that you know will foster guys like that. And that can also accept roles as maybe not being the starter. There's a lot of guys that were starters in this league and you try to bring them back as a backup that's really, they're not looking to foster anybody. They're looking to beat the guy out and keep their job. Eric and Dean are two guys that, yes, do they want to start? Do they want to play? Yes, but they also are the type of guys that you know are gonna be good for the younger guys too because they will help them any way they possibly can. I mean, all these guys are in competition with each other and takes a special guy to be able to mentor somebody that's in their position. And those guys are special guys. And I give credit to Arthur and Terry for bringing Eric back in that role and bringing in Dean Marlowe. Well, if we can somehow put the first three quarters of the first game and the fourth quarter of the last game together, that's what we're looking for. Basically, we played really well. I fought for three quarters except for two plays by Hill in the Saints game. And in this last game, we just got ourselves down, but I thought we showed resilience and came back second half. And even when we were down, played hard and gave ourselves a chance to win the game at the end. I mean, it was close. I mean, we didn't give up. Guys were playing hard in the fourth quarter, got them off the field finally in the fourth quarter. We did just played better fundamentally. And what we got to do is we got to find that personality inside us, like in the first half of the Saints game, I thought we played loose, free, not worried about anything, turn it loose, let's go. And we played like that. And then all of a sudden we tightened up a little bit in that fourth quarter in the Saints game and didn't play fundamentally like we did for three quarters. Then in this last game, then all of a sudden it goes kind of the other way and you're down 28 to three. So now all that's the side. Now it's not like we're playing in a real tight game. So now all of a sudden we're playing freer and looser and I thought we played better. So what we got to do is figure out how we can get ourselves to play loose for four quarters and just let the thing go. Oh, no doubt. I can just tell difference in the way we play, the style we're playing with, the way we practice. No doubt about it. And it wasn't even close to me on defense. Players. The way they practice is the way they, you know, get themselves ready to go. When we're playing the same scheme, we added a few things here and there and stuff like that. But it's their attitude, this game's all about players. It did all right, it did all right. Some of the rookies had, you know, they're always going to be growing pains and stuff like that. I thought, you know, for his first start out there and kind of in the heat of the battle and the way and all that kind of stuff, you know, I thought he did well. He's the same thing as all those rookies, they, you know, and Coach Smith always points it out to him, you know, we want you to play like you looked when we drafted, because what happens is that they come in there and they start thinking too much. You know, they're used to the system they are in college and all of a sudden they're playing freer and they're going to, you know, when they go down there to the bowl games like the senior bowl and stuff like that, they don't have them doing a whole lot.  Well, we got to get you back to doing that all the time. And I, but he, but he had a good first start. Yeah, we're just, again, we're trying. Now I had nothing to do with Rashan Evans whatsoever. That was just a matter of let's get him some snaps, get him some snaps when it counts.  So it was like, you know, I don't know if it was 14 to three or seven to three when we put him in, but we kind of wanted to get him some significant snaps when everything's kind of still fresh. It's not lopsided or anything like that, one way or the other. Well, I think you guys feel good that we've had some turnovers in the first two games. We've had, we didn't get any sacks in this game. We got some sacks in the first game. So we've shown progress in that area. Cause, you know, last year at the end of the year, I don't know what we have 15 sacks for the year. And I don't even know how many takeaways we had, but all those things really help our psyche, which to me, that's kind of right now is where it is. I know we're inexperienced at some positions, which just going to get better and better, but it's also, it's just our attitude. And that's, like you asked me, Tori, it's, that's why I feel just so different about this defense. I just, I feel good about these guys. I do. Oh yeah, he's one of my mentors. One of the reasons why I coach special teams is because of Joe D. I interned for him when I was with the Chicago Bears, when he was the special teams coordinator in 13 and 14. So there's a huge reason why, he's one of the reasons why I coach special teams. Yes, amazing guy. Man, I was a defensive line coach at the time, 2013 at University of South Dakota. And it was the very first Bill Walsh fellowship program I was part of with the Chicago Bears. So it was cool just learning the different ways on how he taught personnel, concepts, teaching, a lot of the core things that I do on special teams and from teaching roots back to when I was with him back in 13 and 14. So I really admire the way he coaches the game and how he gets guys to play really hard. Yes, I mean, you look at the game, special teams, 40 or more yards being exchanged, a direct attempt to put up points and a change of possession. So anytime there's a big play made on special teams, it helps the offense and defense, you know, whether it comes to field position or putting up points on the board. So those plays are big. When we talked to our guys, guys like, you know, Cradell Hodge, just as walking by right now, we said great plays are made from great effort.  We try to put the best 11 out there, but plays on special teams, if you're able to make a play in space when it comes to playing a special teams game or being able to get a hand or a stomach on the ball like Troy did, those are momentum changing plays. Is that no beauty that you need to have some success, have some plays all your way? Yeah. Yes, Dave, when it comes to that, when you're playing in space and you get the opportunity, one, it goes to guys just doing their job. Once they do their job and they're able to put their stuff in a position, now it's up to them to go make that play and then provides credibility. And then those are opportunities for guys that lead by example, by their play and their actions and their production on the field, Dave. That makes sense. Just like any other week, our kicking coverage have to compliment each other when it comes to the coverage game, D-Led, being able to, you know, put the ball where we need to put it at, getting our coverage guys to strain and coverage, get off blocks and finish on the football, with leverage and attack the football. So it's like any other week, it's the NFL. There's always dynamic returns that we go against each and every week. And it's our job, the 11 guys that are on the field, to be able to, one, put the ball where we need to put it, to when it comes to coverage and kick placement, and then our guys getting off blocks and making plays. They have a lot of top special teams guys when it comes to Seattle's special teams. I really admire what Coach Izzo's doing there, their direct reflection of how they, how he was as a special teamer in the NFL, you know, nine-time all-pro, special teams player, pro-ball player. And it's cool to see how he's coaching those guys, and it's a direct reflection of how they're playing the game. They're fast, physical, aggressive, and they really compliment their offense and defense, D-Led. They're, both of them, both were pro-ballers in, you know, a couple, like, you know, at Dixon was, I think it was 18 seasons pro-baller, Myers pro-baller 20 season. Both, both guys are very talented, but they'll have very strong legs, and you can tell that they really do a great job of one, putting up points, or flipping the field when it comes to their coverage units. Mm-hmm. You know, when it comes to pump pros, our personal protector, and as everybody's correlated to our protection, we want to make sure that we over-communicate and that we're clear with whatever we're doing, whether it's Keith Smith out there, part of our pump pro, Eric, Eric Harris, if Cordell Patterson wants to get back out there and play the gunner position, hint, hint, just messing with him right now. But those positions, everybody is part of our pump pro. They got to over-communicate and make sure we're all on the same page. We want to take our time to make sure that we're clear and concise with what we're doing, and then we're doing a good job of protecting opinion so he could go out there and do his job and put the ball where it needs to be. I mean, we got, you want to, there is a, we're not going to be naive. There is awareness to it, but we're not going to allow the external change who we are internally and how we go about our business. We get to play on a hundred yard football field and it has two field goal posts and there's one football. So we go out there and we do our job at the highest level possible and we make sure that we're complimenting each other on all phases and it doesn't matter and we'll love the outcome of the game. How many special teams are you? He's a four-phase player for us. You know, he played, I mean, he was one of our better special teams players. You know, the two rookies that we, that played a lot for us was Avery Williams and Richie Grant when it came to special teams. He was frustrated with himself a little bit that he wasn't getting more defensive snaps. Did you ever see that carry over to his special teams? No, never, never. I never saw that, Josh. The guy's awesome. Of course, a player that plays on defense or a player that plays on offense, that's their goal to be a starter on that side of the ball. But he did a great job last year when it came to special teams. Him and guys like Avery, they would come to my office every Monday and Tuesday during the season and make sure that they were dialed in and what they needed to do for special teams because they knew that him being a part of coverage games, which he was one of our top tacklers on kickoff, correlated to us having great field position on defense. He was never, you can have your frustrations but it never bled into what he did on defense and it never bled into what he did on special teams. And that speaks high about his characteristics. You say he's awesome. Why? Other than that mindset, what else makes it? He's a competitive, ultimate competitor. He's a competitor. He's physical, positive attitude. He's a leader. He leads by example and he works hard. He, I mean, his urgency on the field and the way he goes about his business. And again, I would say the top thing, he's a competitor whether he's, he might be outmatched sometimes going against a bigger opponent but he wants to line back up and go against that guy again. And every single play, he might lose a rep but the next one I put money that he's gonna win the next rep. He was very solid for us and he continues to get better as long with all these other rookies that we out there playing and other guys, the young players that are playing for us, he continues to get better. He's physical, aggressive, fast. Give him a compliment because he set up that rush too by his alignment and his rush to help free up Troy Anderson. I would like to see those. Be a good champ. Yeah, those are conversations that we have with our specialists and the head coach and myself. And when we get to eight, anytime we play at a stadium that's outdoors, we want to use, I said this before in one of my press conferences, we want to use the win to our advantage. So there's a conversation that we have with Kub, Bradley, Liam, Coach Hoff and Coach Smith and myself. We have those conversations on how we can make the win that day a factor and help us compliment us on all six phases. Oh, I mean, I think it's a combination of things. Obviously, when you design things, predicated on what you think the defense is going to do, there's times where obviously defenses take certain reads away and the ball goes where the ball goes in terms of the progression for the quarterback. So Kyle is doing everything we ask. Obviously within the scheme and the fit of the games, trying to get him involved is obviously important for all of us. But there's other players that obviously have shown up, made plays. It'll fit within the flow of the system. In my experience, when you start forcing the ball at the quarterback spot to somebody, you're asking for bad things potentially to happen. That's not how we speak with the quarterback. Obviously, defenses dictate at times when the ball goes, but it's not a forced situation.  There's a certain criteria in which we ask guys, especially mentally, to go out and perform. You have to be able to be on top of that.  There's things that we ask. He tries to execute at a high level and the ball finds him at times and he makes plays when his number's called. Yeah, I mean, I think within the flow of the game, situationally, where things can occur. Sometimes there's different things that defense presents, which we try to take advantage of. But there's no set going away from something or going towards something. There's definitely a flow of the game in which things occur. Is that all your scripted questions? Okay, now we're ready to go. I felt like that was intense right there at that point for D-Lab. Well, I do think it's a consistency, right? In which a player shows up every day for work and with him and a number of our players, regardless of age or experience in NFL, there's a certain level of standard in which we expect them to come in, understand the plan, come out here physically and execute the plan. But just even how you approach the meetings, the walkthroughs, it's professional football. So there's definitely a level of, when you come in to understand there's a responsibility in that. And I think Drake, just like our other guys, take that responsibility serious. And again, you hope for not just him, but other players for their production to continue to occur, because obviously that makes us a better offense. Does that feel right? Do you feel better about it? I mean, for me, I know last year is last year, but in terms of the confidence thing, there was never a waiver, regardless of the timeframe we're talking about. I do think there's times in games where certain things work better than others. There's also times in the run game or pass game in which your mashups dictate things. For us right now, it's early in the season. And we're two weeks in, there's 15 more you're guaranteed. So we're still, for us offensively, right? We're still going through some of the things to make sure we're putting our players in the best position as coaches. And it's no different going in this week. But do you feel better? Yeah, I never felt, in my opinion, right? Going forward, I don't look at it. Did I feel worse? I did feel better. I mean, each week we're going out and trying to attack a defense a certain way. So in terms of wavering and confidence or feeling better, that isn't coming to my mind. It's more about how do we attack the scheme that we're playing? And if that week calls for us to do something different, then we're gonna do it. But we're trying to score points regardless of how we do it. That's the name of the game and an offensive side of the ball in the NFL. And regardless of how you get that done, we need to get that done. Well, there's different pieces, right? Obviously, there's different skilled positions. There are different offensive line positions from last year. But again, it's still early in the process of this season. There's things that we have done this year that we've set out that have been better. But there's things, obviously, when it comes to certain things that have not allowed us to score, that we need to improve on. And that's the case day in and day out. Today, obviously, Thursday, you're working on a different situation than you'll work on tomorrow. And these situations, you know, critical or not, those are the things that we need to improve on. And right now, that's the message going forward to the offense. So I feel like where do we need to go? Yeah, I think when you talk about the quarterback spot and not to be a, not to make an excuse by any stretch, but the realization of the quarterback is everybody has to do their job to get great quarterback play. And the quarterback has to do his job. And so that could be anything from assignment to alignment to when the ball snapped to execution. And so when you single out one position, to me, it's obviously in my position, it's about everybody. It's all 11 guys. So for us moving forward offensively, it's about getting those guys in sync on the same page, making sure that we don't beat ourselves. And then we execute at the most stressful and tight situations that games will allow us to execute in. So for me, with Marcus or any other players, it's about how we actually represent ourselves as a unit and go out there and execute in those critical situations. Yeah, I think it's a great question. I think, you know, for us, what we ask our players to do, what our mindset is from an offensive standpoint is that we do cause mental stress for those guys that push their limits. We think it gains us an advantage, not just in the formation, but the person else. We expect anybody who has a helmet up to be ready to compete and play, and we wanna use all our guys the best of their ability. And so that is really the mindset, regardless of age or experience, there's a certain criteria, a standard in which we want those guys to uphold. And for us, obviously, as coaches, to make sure they get to and hold them to. But we look at it as an opportunity for those guys to go out and highlight what they can do, but we're gonna push them. We're gonna push them physical, we're gonna push them mentally. And guys understand that they accept it. They accept the challenge. They like the challenge. You can tell by the feedback or how they go out and practice. You gotta be locked in and guys do that. And we appreciate that as coaches. Wait, you're still on script? No, we're off script. Yeah, we're off script. This could go anywhere now. Yeah, this is iCloud, iCloud. All right, here we go. Yeah, yeah, I think another young player that we had just mentioned about, again, we look at him as another piece of the puzzle to go out there and execute what's called upon. If you saw in college how he runs the football, I don't think he changed just because the name on the helmet changed for him. There's a certain level in which we expect him to play just like all our guys. And he's fit that so far. Yeah, I know these guys decently well. Just some of these coaches and the defensive side I've worked with before. I've got a ton of appreciation and respect for Coach Hurt, Coach Desai. You know, they definitely bring a level of Coach Hurt is an intense, smart football coach. And you can see that the way his defense plays. I know when I was with him, I know how he went about his work habits and I appreciate him. And I know that's what he's stressing on his defense. I know Coach Desai who was brought in there. I know how smart he is. I know what he is able to bring to the table. And I think it's for how they're going about their business, it's a very good fit and it's gonna cause and pose a big challenge for us on Sunday.


Taking a step back, that's what Trinidad and Tobago Batsman Darren Bravo says he will do after being snubbed off a place in the West Indies One Day International Squad for the upcoming series against England, which bulls off on Sunday in Antigua and Barbuda. This is what he posted to Instagram at the weekend. I've taken some time to ponder and wonder what's my next step moving forward as a cricketer. At this point in my career, it's not easy or should I say it takes a lot to continue to find the energy, the passion, commitment and discipline to be able to perform to the best of my ability and to put myself in a position to make my return to international cricket. Without any level of communication, I've been left in a very dark place. At the moment, there's three teams representing the region in multiple formats or series. That's approximately 40-45 players and if I can't be in any of these teams after competing in our regional tournaments and scoring runs, therefore, they are basically telling me that the writing is on the wall. I'm not giving up but I believe it's best to step away just for a bit and maybe make some room for a young and upcoming talent. I will end by wishing each and everyone all the very best. I've lived my dream. 46. Hashtag, age is just a number. All right, so Ricardo, you know, this has been a discussion on the Sportsmark Zone. We had DJ Bravo, the older brother of Darren Bravo on the show when the news came out of the team, you know, being selected against England. Darren Bravo has posted this on Instagram now. You stand at the top of the show, he appears broken, you know, he's rejected based on the fact that I would think he's done what was required of him because in order to be selected for the windy squad, you have to contribute in the area that you're specialized in in a really good manner to be in contention for a spot. He was the top scorer in the Super 50 tournament this year and the year before. We sit here now with broken Darren Bravo, who feels as if he needs to take a step back and think about what's next because he would feel, Ricardo, he's done everything that was required of him except his age and to me that age is still a different topic of discussion because other players very close in age to him got selected. What do you make of it? Yeah, so as I said earlier, Mariah, when I read the post from Darren Bravo, I felt the frustration, I felt the hurt, I felt the disappointment that he is not in this one day international's quantifies England because as you pointed out, he has done everything that the selectors or the selection policy in my opinion would have asked of him. You look at his contributions in the last four editions of the regional Super 50, for example, where he is averaging 83, 48, 95, 96. Every single one of those seasons, for example, he has a better average than let's say Kieran Utley, who is in the squad. Kieran Utley is 33 years old. Bravo is 34 years old. Desmond Haynes has said that Bravo's omission from this squad is because they are looking to the future, because they are looking to the next World Cup tournament. So is the suggestion then that a 33-year-old Kieran Utley is in your plans for four years down the road but a 34-year-old Darren Bravo who is scoring over the last four editions of the Super 50 significantly more runs than Utley is not in your plans. There is something wrong with that as far as I am concerned. This to me, Mariah, is the type of decision that selectors can and maybe even should be sacked for because the explanation is too far wide from what you have done on a whole. It does not make any sense for you to say, Darren Bravo is too old and is not in our plans for three years, yet you pick someone who is a year younger than he is. 33, 34, what's the big difference when Darren Bravo has been making significantly more runs. So that in itself is a problem. I also take note, Mariah, of the cricket West Indies president, Dr. Kishore Shallow, coming out and saying there is no policy on age or there is no policy on excluding any player based on their age. That's significant for me. Also what I consider to be significant, Mariah, is what Desmond Haynes said about Darren Bravo back in February and I want to remind our viewers of exactly what he said. This was during the regional four day. Bravo had just come off a pair of centuries and this is what Desmond Haynes said. Being the Caribbean, we don't have the luxury to really talk about age is not on someone's side. If someone is doing very well in our competition, I think his name must come up for selection. I think Bravo has proven that he can play cricket at the highest level and it's just a matter of if we can get the guys if they're committed and they really want to play. But Bravo is a class art. I mean, he has done well at test level at a period where he just lost his form and so forth. But we know that Bravo is good enough to make runs at this level and we will continue to monitor Bravo's progress. So I'm not saying that you can't change your mind, right? But in February, we don't have the luxury of not picking our best players, which by the way, I completely agree with. I don't think West Indus cricket has the luxury of not picking your best players in the moment. I don't think any team that is losing consistently has the luxury of not picking your best team. You pick your best team in the know, you get the results and you build with those results over time. Dwayne Bravo mentioned it last week as well. At the end of every World Cup cycle, the selectors speak about building for the next World Cup, building for the next World Cup. But then you're either struggling to qualify or if you manage to squeeze into the tournament, you're finishing ninth or tenth. So you're not seeing the results of that quote and quote building process. Why we are not thinking about picking our best players and getting as many wins as possible is beyond me. So what? You are willing to go another three years continuing to lose? That doesn't make any sense to me. So that I have a problem with. But in February, Mariah, Desmond Haynes is saying we don't have the luxury of not picking our best players, but now he is singing a completely different tune. I also note that in February he was speaking about it's about whether the players are committed and really want to play. And so I have to be thinking to myself because nothing else makes sense. Is it that the selectors don't think that Darren Bravo is committed and really wants to play? But he has appeared on the show and we've asked him about his commitment and he has expressed his desire publicly to of course represent the Western East. One of the things though when DJ Bravo was on right, Layton asked Bravo because of course when we do interviews, we were at the core trying to get a balanced interview. So Layton brought the point Ricardo about DJ Bravo. Thank you for that. Darren Bravo representing in the Super 50 and not giving the same performance and win these colors, right? Layton asked about that. And DJ Bravo of course explained that he has not been afforded numerous opportunities to do that because when he gets the momentum, of course, he's dropped. So for me, I'm wondering if that was a consideration when the selectors... It shouldn't be. It shouldn't be. And I tell you why Mariah, in my opinion, Darren Bravo and in a way he admitted this when he spoke with us as well. Darren Bravo was prematurely brought back into the West Indies setup. And so his results, especially in Test Cricket, he had that horrible tour of New Zealand, for example, after his return. I think he averaged about 13 in seven or eight Test matches. But in my opinion, he was prematurely brought back into the setup. He got dropped. He should have been dropped at the time. But the fact is, Darren Bravo has gone back, worked on his game. He has improved significantly. He has put the performance is done both in 50 over cricket and four day cricket at the regional level. And I think deserves an opportunity at this stage to be back in the West Indies fold. Now, if you select him on the back of his performances at the regional level and he goes to the highest level again and does not perform, then we can have a different conversation, Mariah. But you cannot look at Darren's numbers at the regional level in recent times and judge him based on what was happening four or five years ago when, as I said, he was prematurely brought back into the fall. And when we spoke to him, he did say he got selected at times when he never thought he was going to be selected. But I think what we have to make up in our minds, and this is where I am disappointed in Desmond Haynes as head of the selection panel. Do we believe that the quality that Darren Bravo has that he can still contribute significantly over a three or a four year period, even if it is going to be a two year period to West Indies cricket? And if the answer to that is yes, then the performances he's put down at the regional level should be more than enough for him to have been selected for this series against England. And it's disappointing that he is not selected, but it is even more disappointing the reason that has been put forward by Desmond Haynes, why he is not selected. So this stepping away from international cricket, this camp, Ricardo, is this a permanent step away? No, he's hurt. This is a 34 year old man who is begging to be heard at this stage because he does not feel that his performances are getting him the required attention from the selectors. And so now he doesn't know what to do. And this is what he does. He goes on social media like we've seen with modern day sportsmen and women. Yeah. And he put something out there. And I completely understand it because as I said, I hear the frustration in that post from Darren Bravo, because he knows he should have been selected. The entire Caribbean knows that he should have been selected. Clearly the only ones who don't know that Darren Bravo should have been selected are the selectors. And I tell you something, Mariah, again, I'm sorry I have to repeat this, but what makes it worse is that you pick a 33 year old Kieran Utley, who did not perform as well as Darren Bravo did in the Super 50 tournament, who has in four seasons not performed as well as Darren Bravo has at regional four day or 50 over level. And then you've shot yourself in the foot as far as I'm concerned when you make selections like that. It does not make any sense. And even the Windy's Academy team and all that, you know, Bravo went on to say approximately 40 to 45 players are playing and he hasn't even made any of the teams, not just the Windy's team, but any of the other teams. And I think that as well, you know, it evokes a lot of questions because, okay, he doesn't make the big team, the Windy's senior team, but what about the rest of the team? Yeah, he's probably not going to make the emerging team, but definitely the A team. If you want to see him playing at a higher level in other conditions, whatever the case, and maybe you think, then okay, send him to South Africa, get him some A team cricket. I mean, that would make sense to me if this is someone you see that can contribute significantly to West Indies cricket, but that has not been done. And yeah, you know, Mariah, I am convinced that there is some other reason that Desmond Haynes feels he cannot say that is stopping him and the selection panel as a whole from picking Darren Bravo, because clearly it can't be cricket. But then you come up with a different reason, don't say age, because I think that's the point I'm making. I'm suggesting that there is another reason that Desmond Haynes feels he cannot say publicly. Now, I would much prefer Desmond Haynes putting that reason out there, but then he would probably say, I am in the media, so of course I want the school. But if you're not going to put that reason out there, then don't come up with the one that you came up with. Do a much better job than the reason you came up with, because that is not good enough. And that makes Desmond Haynes and the entire selection panel of cricket West Indies look foolish. And again, I love the fact that the president, Kishore Schaller, came out and said, we have no policy about not selecting an individual because of their age. Important point. I'll say before we go, Mariah, hold strong, Darren Bravo, hold strong. I think your time is still coming. Give it a few months. It will happen.


I hope that was good for you


Hey, I hope you guys can hear me So this talk is going to be about the product matching problem This problem is something that me and my team have been working for for about One one and a half year now. It's a problem. That's very easy to describe very impactful and Incredibly hard to solve and we have made some progress good progress here and The idea is to talk about the problem why it's important and how we solved it and the learnings along the way, right? So let me start by Talking a bit about index It's best to understand what index does using this this analogy, right? So what Facebook is for people Google Maps is for locations Index is for products What do I mean by that? So basically we are trying to organize analyze visualize all the product information in the world and make it available so that everybody can act on it what this includes is data collection going from unstructured data to structured data analytics on this data visualization and Personalization and surfacing the data, right? So to give you a perspective on where we are at today We are on our way to building the world's largest product database. What do I mean by large? We have seven hundred million products. We have 40k brands 7k and more categories about 10k plus attributes so that's where we are at and We'll soon cross the billion mark, which would be very very interesting Okay, so that's about index Let me just jump into the product matching problem as I said, it's a problem that is very easy to describe and Very hard to solve this is a shoe at five different retail sites It's the same shoe. These are variants so the product matching problem is simply to say that These five products are actually the same product. That's the problem, right now To make this problem a little more concrete I'm gonna introduce some details Let's just assume that I have a crawler and I have crawled a bunch of product pages Let's say about a billion not too much a billion and the idea is for This matching process is to produce a set of URL or a group of URLs Such that the two products two URLs in a group are actually the same product, right? So this block right here is the focus of the talk and This is you know a very intuitive description of the problem So before we You know try to solve the problem. Let's let's get a sense of why it's it's Would solving I mean what what is the value at what is the business impact? So the way we look at it The product matching problem is central To almost all problems in retail analytics The reason is it's if you start with questions like who are your competitors? What prices are they selling the products you carry for? What are the products that you should carry that they are carrying right now? What are the products that they don't carry and you carry and you can promote them? So those are some of the questions that you would want to answer and in order to answer these problems well What you need to be able to do is to be able to match products across stores So if you look at this diagram here, I mean, it's a very abstract view of what what we are trying to do These are products and I'm just trying to put or learn or induce these links across products scale matters in The sense that for this information to be useful You need to have as many products as possible You need to have as many sites as possible. You need to have coverage in a particular category So those are the dimensions of scale and you have to get them right if you are to you know Generate insights over the data. So this is just you know our app And it's just showing pricing comparisons for a particular product, right? But that's just one piece of the pie that you can have when you actually solve this problem well So that's the problem and that's the impact of the problem will now start looking at How can we solve this problem and there are multiple sub problems here? There are many moving parts So what I'm going to do is I'm going to start with each of these parts piecemeal Detail them and towards the end of the talk. They'll all fall in together So let's start with the first problem which is parsing What is the problem? I have an html page and I need to extract key attributes such as titles image URLs Prices descriptions spec text from the product page. I Cannot hand write parsers They'll be very hard to maintain at this scale. I need something that works in an automated way I can develop tools, but it's not going to scale. I need something that works in an automated way So let's try to look at it from a machine learning standpoint, right? How can I model this problem? Well one way to look at this problem is what is the html page? It's a DOM tree and every node here is either a title or not So if I am able to formulate this as a binary classification problem Do good feature engineering For each of these DOM nodes Train a binary classifier, which tells me whether it's a title or not and I'll train binary classifiers for descriptions image URLs Pretty much everything I need. I could attack this problem, right? I could attack this problem in a very Principal oriented as well as a scalable kind of a way, right? Why do I think this might work? I mean what what is the intuition here? So the intuition that I began with was if somebody showed me a Chinese Retail site. I don't speak Chinese. I mean Mandarin that is But I am able to identify the title. I'm able to identify the image. I'm able to identify the price. Why? Because this is about structure. It's not about content It's about structure and if we can do feature engineering. Well, this problem can be solved. That was the Hypothesis right? Well, turned out it does work but there are a fair amount of Details to take into consideration and this is the first learning that feature engineering is key More than the algorithm the feature engineering at least in this particular for this particular problem was key to getting this problem, right? Now let's look at what kind of features just to get a sense for what kind of Things are being extracted. I mean that's the title. This is the variant information. That's the description That's the image. These are additional images, right? That's the price So there are two kinds of features or two families of features that I can extract For each of these dom nodes the first one is Features that I get from the HTML. That's the dom tree You can think of it as a tree. So what are the parents of this particular? Vertex what are their tags? How far away are they from the root? How many children do they have? How many children do their siblings have? What are their tags? There was a massive massive feature engineering exercise and that got us far But if we didn't get there totally, what were we missing? What we were missing is We don't see a page as The Dom we see it Visually, right? It's it's it's an image. So what we started doing is we started rendering the page in a headless browser I found a phantom JS and that allowed us to get a set of visual features What do I mean by visual features? I have x and y coordinates of this dom node after it's rendered I can extract features about the color contrast. You'll notice that the price is The highest contrast on the page often often, right? I don't know whether that's a design thing. It's just something that we have looked I mean noticed in data So with a bunch of these image feature html features and these visual features We were able to train Classification models that allowed us to solve this problem fairly well now there is there is a problem here The problem is that the visual features require you to render the whole page. That's pretty expensive So we don't do it Everywhere we do it for the complicated sites where the html features work. We just use those right, so that's that's how we attack the problem and Solve it. I would say fairly well I mean, there's still room for improvement. There is still room for More feature engineering. There is still room for getting more data an interesting thing that you can do is build a classifier based on these features Have them generate data and use that label data to train this guy That works because you need a lot of label data for it and there are limits to how much budgets you have on crowdsourcing So that's the parsing problem Okay So moving on the second important problem that you need to solve is that of product classification It's a very simple problem again. It's the what is it question? What is that? It's a laptop That's it. What human beings can do very easily. It's hard for algorithms and machines to do So what you're trying to do is you're trying to look at all this extracted data that you have from the parser and Go to a category. This category is typically a leaf node in a taxonomy This taxonomy needs to be very specific in order for you to do a good job at match Any guesses as to why these two products might be confused with each other They're both pumps I've learned a lot about women's shoes over the past one and a half year More than my wife would like which is interesting But yes, this is a pump shoe and that's an actual pump And if you are just you building a classifier based on titles and if pumps has a very high token weight You might get this wrong, right? So Yeah, this is a challenging problem and the key learning here was using ensembles You cannot just use one class of data to solve this problem What you need is a battery of approaches and a good ensemble in order to solve this problem What are the different approaches? Let's start with the simplest one, which is the breadcrumb mapping Data on retail websites is at least the classification can be wrong Sometimes but it is still good, but people have different taxonomies, but if you can map them offline You can use that information as one of the signals You can train classifiers we use linear support vector machines on Titles and descriptions mostly text data you can use a CNN on the image You can also introduce background knowledge now MSc direct would not carry women's shoes MSc direct is a major industrial supply retail outlet in the US, right? So you can you can make a certain Assumption so you can introduce certain features that capture your background knowledge that certain classes of products Will not be available in certain sites, right? so Again, this was a massive featured engineering exercise. It was a massive Ensembling exercise finally we got to a point where then we had reasonable accuracy Enough to make progress on this problem and again, I like to stress the use of ensembles and not Sticking to just one particular source of data There are a few interesting things that you can do here again Where in you can crawl these images or get procure these images offline and then train models and not use the classification online if it pretty much dominates over the text classification So it's important to note the balance that everything associated with images including procuring the data training the models is going to be expensive But it's it's it's resources well spent moving on the next Important problem is attribute extraction Here what you're trying to do is you're trying to go from all this information that you extracted with the parser And you're using a predefined schema to get attributes like brand size color packs so on and so forth Without getting this right. You're never going to get the product matching problem, right? And the schema tends to be very large. It tends to be category specific if you have Classification errors, they're going to bleed into these errors It's going to be a cascade and that is going to be very hard to solve So it's important to get classification, right? And it's also important to get the parsing, right, right? So it's it's like a pipeline but an error cascade So that's what happens in attribute extraction, right?  There's bad and missing data. There is you know a lot of challenges there so Just to give a sense of why I mean these are about training shoes one is from Nike one is from Reebok and If you don't have the brand, right, you could still use the images that will get pretty expensive, but it helps to Get the model that extracts the brands, right in order to solve the problem, right? So we mostly use CRFs for this Typically we are trying to extract so for example a rubber sole so that's sole being rubber brand being Nike Colors can be pretty absurd sometimes. That's the color of the shoe. It's pretty weird But whatever I mean the point is you have to go ahead and define your schema train models on each of these pieces of text that you have To you know extract the attributes that matter for matching, right? So that's that's the next problem now Fine, I mean You're at a point wherein you can actually start attacking problem. You have the original data that you started with That was parsed. You have the classification category and You have your attributes. We refer to this as an enriched product record. It's a product record with all the information So I guess one way of attacking the problem is to say I'm just gonna come up with a distance metric and I'm gonna do, you know an all-pairs on these products and If that distance is small, I'm just gonna say that's a match It's a pretty reasonable approach The problem is with our scale it would take roughly around 18 years to do that And and square doesn't work and square doesn't work in the sense that at this scale Even if so this I mean I made the assumption that the particular pair-wise matching would take a nanosecond and Then it's 18 years. I guess our funding would run out pretty soon I mean, you know not class for 18 years. So we had to do this faster, right? But you know, it's it's not a big deal in the sense that you have all the information that you can use in order to You know do this. I mean solve this problem better What do you want to do is block on a bunch of things that Will take your products and put them into groups such that the probability of any two products being the same is high So this this step is essential You can do blocking in many different ways you can say my classification and brand extraction is really good. I trust it Which is why I'm going to put all The products that belong to a certain brand and a certain category in one audition It's a simplistic approach depends on how much you trust your classification depends on what particular level in the taxonomy You you want to block act so you might say my leaf level accuracy isn't good enough But one level about that. I'm good. Let me block at that level So that's that's one way and you can take different approaches for different top-level categories, right? The other two approaches are basically based on string similarity join and hashing The key here is no one approach again works You're not going to be able to take one particular way to do blocking and Extract or get good recall on your matches You're going to have to try different approaches and then you're going to have to merge these groups to get the final set of groups On which you can you know do a pair-wise inference. So the learning here is One doesn't work again. This is an ensemble, but I won't call it that because that's more from a classification, you know terminology as such Okay, so now you have these groups and what you need to do is Simply do a pair-wise Computation right a pair-wise distance computation again that would require a pair amount of feature engineering, right? And then you're going to have to do Constraint clustering and let me get into some some some more details But let me first talk about the pair-wise distance computation. I mean what do you have? Here is you basically construct a Distance metric you can learn this metric or you can define it based on background knowledge it basically is going to take two products and you're going to construct these Representations based on all the data you have so for example, you might take the title and convert it to a bag of words Titles across retail Websites are not the same not exact matches So you have to be fairly smart about that you might remove some stop words that don't really matter For example, it's a good idea to remove the But turns out the North face is a brand North face is not a brand the North face is the brand, right? So you can't really remove it in all cases. That's tricky But to cut a long story short what you can do is you come up with a distance metric or learn a distance metric That allows you to do you know to get a distance between the two products that you have in one of your groups, right? Now you could then given that you have this distance metric go ahead and just cluster these products and Then whatever works or whatever ends up in the same cluster is what has matched, right? That's that's a match. So you have done your blocking you have used all your attributes and you're good to go the problem is It's it's that there is a extra layer of complexity to it Certain products just have to match Certain products cannot match. When is this suppose? I do have UPCs for products I do have the MPN the manufacturer part number for products. Let's say I have a product line for certain products I'm gonna have certain background information about these products That I have to leverage in order to I mean I can't ignore this information this information a allows me to do better second it also allows me To impose certain constraints. So for example One product at a store can only match one product at a different store. This is the match at a store constraint Now it's possible that a duplicate product exists on the same store Marketplaces are nightmares with this but With these constraints and with a good distance measure you can basically do you know constraint clustering And in its it's it's fairly simple. What you're saying is you start, you know bottom up You start with one product clusters You keep merging them and just make sure that your constraints are not violated in the sense that these two things if they have to be together You just go ahead and merge them immediately if they cannot be together you prevent two clusters from merging, right? So that's that's one of the ways to solve it there are certain, you know You could improve the efficiency of this but that's that's the fundamental idea But the key learning here is Just just distance metric computation and cost clustering based on that Doesn't work and you'll get absurd results and they'll keep You know popping up all over the place what you really need to do is constraint clustering So that was an important learning so now we have all our moving parts in one place and We can you know look at it, you know step back a bit, you know, how does this thing work? So we start with a bunch of html's we parse them to get a product record Classify the products extract the attributes block and construct product groups do match inference to get the matches So that's that's the flow that's the data flow and each of these blocks You know contribute to the final process of matching Right. Let me just talk a bit about how we go ahead and evaluate this. I mean, it's pretty much standard precision and recall but You know, there are there are a few nuances here in the case of precision If given that you have a good spot checking budget, this is easy to do you have a bunch of reported matches You can go ahead and decide on what sample to take based on, you know confidence interval that you want and just send it for spot checking and get a You know get a sense for how precise your matches are you might want to do it at a customer level at a Site level at a category level at a leaf category level you want to segment This particular population of reported matches so that you are able to really find out where you are doing Badly this is one of the big problems because what happens is if you just take one random sample from your entire set of reported matches And that number is high that doesn't tell you How good you are doing with a paying customer? So you're going to have to sample here sample his products or you know separately right that that's and I mean That particular customer and their competitors. So the key idea here is if you care about a certain segment You've got a sample from it and then given enough of a spot checking budget a crowd sourcing budget You might outsource it you can get a very good sense of the precision. This is a good problem I mean this this can be very easy to evaluate. It's it's not hard just throw money at it the recall on the other hand is Extremely hard to estimate. Why is this is a needle in the haystack problem, right? How many matches would I get if I did all pairs? There's no way to find out the population of all pairs is so large as I showed in the plot If I go ahead and sample from this population. It's the population of matches is going to be extremely rare so This is something that I'm trying to you know, I mean we're really trying to work on getting a Good estimate of how many matches are really out there Some of the ways that we have used is to Have people actually search for products on different sites where we Expect them to be sold to put together a blind set and then we compare our results against this blind set Again, it's it's a very rudimentary approach But it does allow us to get a sense for where we are recall wise The other thing that you might do is say that I'm going to construct an envelope an envelope of Potential matches, so I'm going to say anything that is within a string, you know a jacquard similarity of Whatever I'm going to consider it as a match and I'm going to sample from that population and then You know treat that as the bounding envelope so a lot of ideas here, but it still remains as a Needle in the haystack kind of a problem hard to estimate But in general, I mean people are taking pricing decisions on this so they care more about precision So I mean we have that part covered Okay So towards the end, I'll just leave you with you know One one key learning, which is not that deep is that In order to do all of this you need to build the right set of tools looking at your data You know annotating data Looking at similar images and we start doing this with you know little bit of scripts and a little bit of you know excel or whatever and That just gets out of hand pretty quickly. You need this entire ecosystem of tools In order to do the machine learning stuff, right? Right, so I guess over a period of time. We have built a lot of tooling around being able to you know Download this data being able to identify similar images similar titles cluster them so on and so forth plus have people be able to Annotate this data generate training data. So I would say tools matter. I mean build them early on I mean because hey the longer you try to make do with back tools It's it's going to really impact your velocity again It's a very trivial kind of a thing but in the long run it matters matters a lot, right? So that's pretty much it. I'll I'll leave you with what index does and I'll take any questions you have Hello, really nice talk. Thank you and I'm right here. Oh and couple of Points, so you were mentioning that the feature engineering was really hard. So I was wondering if Back propagation was a way to identify using some You know deep learning algorithms, you know, they will do it detect the features themselves Is that an option for the first problem the feature engineering and also one of your slides had something CRF? I didn't understand that Okay, okay. Thank you. Okay, so to answer your first question I mean that's that's a very important question that I get asked By my stakeholders on a day-to-day basis feature engineering constitutes somewhere between 30 to 50 percent of all our activities The hope is that deep learning fundamentally replaces this step. I See this happening in the case of images Really fast and really well in the case of text. I'm sure it'll get there but not not that rapidly But again, you know things things might change, but we have had really good Experiences with doing deep learning with images rather than with text Hello So about the slide where you're matching products and you're comparing to each other based on their attributes so What about something like? So suppose if there is a Nike shoe you can always get the catalog and get group the products according to like catalog from their official product listing website or something like that and Find the distance closest to that catalog and just group it accordingly so catalogs or crawling or getting data from manufacturers a I mean, they're not willing to share that data. They have current data They don't have data that they'll share over, you know a long period of time Secondly the parsing becomes a more notorious problem So it would be great if you had feeds for all of this and if you were able to use these feeds and I guess you're going to venture out into that but in this particular form We were trying to solve the problem purely starting from HTML pages But the thing is so that both come under different categorizations of data, right? So one is the meta so that will be something like a like the metadata So that's like the product listing of the catalog like you can treat it as a source of truth And all the other things can be treated as something like transactional data or the data which is required for processing So there can be other things for which can come under the metadata category as well like a list of recognized brand names So like you said the north face, right? So that they can be okay These words are definitely recognized brand names if you have certain amounts of metadata So do you go with that? So again if you are Google people will share this data with you willingly if you are a small startup They wouldn't that's one reason second Our entire line of data products around this product database is about competitive price intelligence Why would a retailer give me information retailers would never share information, right? Unless there are you know for some other reasons They are customers or whatever Secondly Manufacturers again have no incentive in sharing this information. They'll have it on their websites. Sure That's because that drives sales for them, but I mean I agree that in a sense It is an artificial problem in the sense that if you had that data if you had a good clean structured data All this wouldn't be required. That's Correct, but from a purely business standpoint This data is very hard to pick procure and that is the whole reason why we do this the hard way I mean it's a business reason rather than a technical reason. So yes every retailer and manufacturer if you had access to all their data This wouldn't be a problem Thank you. Yes You index these separately in that case the UPC might be same across products So do you have you face challenges and how did you address those? That's a very good question. I'm sure you actually play with this data. That's why you asked that question Packs are notoriously hard packs boxes items We have models that individually recognize these and put them into attributes So again to give a context for everybody a retailer will often take five pens Treat them as a single unit of sale and then sell that right The price of this is going to be well five times of the price of a single pen But on this on the page, you're just going to have pack of five somewhere there so we do attribute extraction for that and That's how we solve the problem and getting that attribute extraction step is critical. Do you work around that? How do you work around that problem? so Data around UPCs is this sanitized based on the You know the titles the images that they will have right and once I mean there is a process of sanitization to UPCs No piece of the single no single piece of data is Gospel right so you do have to sanitize that but if you have a high level of confidence Then you can probably use this data So yes in the sense that there have been enough and more cases where in UPC data was bad But the way we take care of that is we we look at other pieces of information also Sanitize it separately. So next question is Is it done? Hello So next question is about category finding where we are doing bread come mapping. So that Taxonomy building is basically how we are building that taxonomy Okay, so I guess I I mean I would I can point you to somebody's master's thesis out of IIT Madras Who who actually worked on this problem very recently? I guided him so to give you a very small but I Good answer to this you start with taxonomies from multiple different places you take you train Classifiers for each of them right then you take a product from one store and see what classification it got here And you take the classification You know you take a product from here and predict based on that taxonomy What that allows you to do is to construct a matrix Where in I have a category here and a category there and then based on this kind of an analysis You can build a richer taxonomy. So let me give you a very specific example This is a very interesting problem There is a site called sweetwater which does just does music instruments now their Taxonomy when it comes to music instruments is way deeper than what Amazon has so when I'm constructing my master taxonomy I want their entire tree to go sit Right in the place where Amazon is so this kind of an automated approach of training classifiers on each taxonomy Have them make predictions on other products and then take a look at the breadcrumbs of how they align is The approach that you might use in order to construct this You know this really large taxonomy again. It's not perfect. You still have to sample spot check validate But that's that's how we attack that problem Yeah, okay. Yeah, sorry So I work at Lazada. We're part of our business We're online retailer part of our business model is one of those marketplaces that you hate So and we don't do that to you on purpose, okay? This is the data that we get from our sellers It's often dirty and messy So we're going through the actually the same exercises that you are Trying to entity match all the data and recognize everything and get it nice and neat and orderly It was great presentation. Fantastic. Thank you Sort of stuff that keeps me awake at night thinking about So in the process that you're doing this how often do you turn to? Manual people to QC this so at some point you're going to get some certain level of confidence That you can be able to push things into yes This is a match or no this isn't a match then you're gonna have some middle ground and you have to spend some time With human people doing yes knows Okay, what I mean roughly what percentage of the products fall into that okay, and can you use that training? Data to actually improve results over time. Yeah, so to answer your question We have segmented all our our data Or all the inferences that we draw from our data into segments based on how important they are So if a customer is looking at a certain piece of data If they are a paying customer or a POC customer, that's the highest tier, right? If there are prospect they're the next year Then there are certain things that we care about from the point of view of scale if we want to you know for many different reasons So basically We have broken our data down or broken our you know the output of the process down Into these tiers and each tier has a certain confidence of interval confidence interval that we want to operate at Based on that interval and based on the you know How I mean that basically represents how much we care about it There is a frequency at which we sample and there is a sample size that that we take So the resources that we have are distributed based on these tranches And we are sampling and spot-checking all the time because Yeah, I mean it's it's one of those things especially when it comes to prices. Yeah, we do it I mean we are very paranoid about this in terms of budgets I mean are you asking that question in terms of budgets or I mean In terms of the number of items spot-checked So typically for a 95% confidence interval with a 50-50 distribution with a margin of error of 5% a Sample size of around 700 is good enough. That's the number I remember or the top of my head But then again those are the three numbers. I mean what is the underlying distribution, right? So if it is a rare population, that's a problem. What is the my margin of error and what is my confidence interval? So we start with the margin of error and the confidence interval We have a sense of the underlying distribution and we let these two numbers guide it So for example, there are cases wherein we want to operate at a 99 confidence interval and we want 2% errors. I think the number that was asked for was 97% accuracy I don't know why it was 7 or 6 or whatever. There was a number that we need. I mean, that's an SLA, right? We need to meet that. So based on that we do you know the sampling and the spot-check Yeah, hi here So Mayan is a follow-up question on what he asked more on the quality side of the whole process since we have a series of Tasks that are that have a chance of something going wrong somewhere So I understand you have manual QC as part of every phase. How do you ensure? Have you also automated the whole quality process? How do you ensure that everything is just right to you know, go ahead with Okay further, you know business Notes, right? So I guess one of the parts of his question that I didn't answer would might answer yours Every single model that we have is calibrated in the sense that it produces a probability This is the single most important thing that you have to do in order to get this thing, right? What the probability gives you is the ability to look at low You know Predictions made at low confidence interval at the low confidence send it for spot-checking completely You know remove them from your output use that and have it annotated use it as training data So for every stage on a continuous basis, we have how many you know predictions were made with a low Confidence and this thing directly goes into you know the crowd sourcing pool as such, right? So yes, this process is completely automated All we have is hey, there's an API Submitted it goes there the number comes back. There is a dashboard if that number is read then I know I'm not getting sleep tonight right at that kind of a thing Yeah, please take all your other questions offline. Thank you Nikhil


Okay, so let's look at taking these two progressions now one four one and one five one and stringing them back together So we're gonna stick around and see I like to stick around and see for explaining things just cuz it's doesn't have accidentals So it's a little easier to understand so Now anytime I'm I'm improvising at the piano, and I'm practicing voicings. I find it's actually Useful to try to shape the the practice any anything that you play here Pretend it's like a little piece of music and actually try to make it sound good Think about your dynamics think about your articulations think about the phrasing of things So let's start with So we just did a one four one I'm holding the bass I'm gonna do one So all I'm doing here, and I'll start to write this out the treble clef there bass clef connect them I'm holding out a bass note. Let's put a time signature so we can see Holding out this bass note here. I'm actually playing octaves so I've got two C's And I'm just playing quarter notes In the right hand so you can see that one two just like that now. I like to Practice writing this out quickly and accurately And then here as I talked about before I'm actually holding out this bass note The voices are just changing a little bit here. You got E Moving up to F And you got G Moving up to A This is just back down now when you're sketching you can use shorthand And sometimes if I'm repeating a thing And it's really obvious to me what it is that I'm trying to accomplish here I'll just write a line following the chord that way don't have to write the whole chord out every single time And then same with If I do another measure here I can just repeat that with the repetition sign Just like that So here we've got our first phrase of melody to the bass An eighth note right there It's not super clear yet and then just Now for over here what I'm doing is I'm doing a G in the bass You'll find I like to do a lot of Shorthand for things if it's if it takes a lot of extra effort to write a bunch of like, you know Ledger lines or to copy a thing multiple times when you're sketching in particular You can save a lot of time try to get your idea down quickly while it's still fresh in your head But here I've got G chord so that's Actually, you know, I think I I didn't do that quite yet if I remember what I played Was just a straight up one five one. So I'll do See you again And I suppose we could do the ledger lines. So every time I like to think of what an octave is in ledger lines There's three ledger lines And if the top note is has a line going through it and the bottom note Is open and vice versa the bottom note has a line going through the top note It's going to be open just a little shorthand for my brain So we had Okay, so what we've done here is we've written out a complete phrase I don't know if we can see here working on a getting a better camera rig set up for my notation But for now this will have to do The one of the big benefits of Doing things from Musical, you know Patterns or progressions that you know well Is that it takes care of a lot of the mental load that is required for being able to take what you hear in your head And getting it down on paper So not only do I know what this sounds like before I play it I know it's a common musical object But I also know how to write it down With one five one You can see how I I've got this repeated a couple times But if it changes Learning to write what's in your head and Or hear what's in your head and properly write it down on the paper is a skill that takes time to develop But this is a great way to develop it. You you practice Playing something thinking of something having to remember it Transcribe it down if you can't remember what it was exactly Go back to the keyboard try to play it again try to get it accurate And then from here you have a lot of flexibility. I mean we can we can look at these notes and then we could Come up with a melody And I all I was really doing was you know picking Different notes And it's really all part of this chord anyway And you'll find as we get deeper into composition you start to really play around with you know, what's going on there All I'm doing here is now connecting these These chords with passing tones But you'll find that a lot of melodies are not that much more than just chord tones and passing tones And some of the more advanced types of ornamentation or advanced non chord tones Um They're they're much easier to Kind of control when when you keep things simple at first and you write melodies that focus on chord tones And connecting those chord tones as you go