The Substitution That Changed the Game. 3 Games, 3 Wins. Charlton Top of the League.
For those who like data driven analysis. I'll be analyzing every post Charlton game.
Here the link of the youtube channel in case you want to follow up: https://www.youtube.com/@gaodelacalle_en/videos
Comments
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Very interesting.
I would say though, that comparing Alli‘s data with Carey’s at the beginning of the video doesn’t quite work because Alli essentially replaced Grant on the wing while Grant moved into the centre to replace Carey. If you’d compared Carey to Grant’s second half and Grant’s first half to Alli that might have been more enlightening!
Dont want to be too critical though. Some nice analysis mate!😎👍🏼1 -
I'm really ambivalent about this stuff.
Data driven analysis is fine. I worked in that area for many years. But these videos feel full of retrospective data fitting. In other words, it seems as though GAO is looking for the numbers that support what he already knows or what he felt that he saw during the game. It's telling that a lot of his 'conclusions' come where he ignores some numbers if they don't support the pre-determined opinion.
There are also lots of simplifications. Lloyd Jones ... identified as 'top defender' even though (or because?) he made no tackles. Lindsay and Clarke of Preston had total scores equal to Jones, but ... heaven forbid ... they had to make tackles to do so. I also think that Leaburn featured quite highly in GAO's 'top defender' list, but he wasn't mentioned.
On that note, I though that Leaburn had an outstanding game. Again ... not mentioned.
These post-match analyses are great fun and I know that many people enjoy them. But let's not get too focussed on what the pseudo-science might be telling us. I came away from the game knowing that Alli had made the difference, that Grant's mobility was crucial and that McNamara and Leaburn did us proud. But that view wasn't based on any numbers.
Here's my challenge, @GAO. Get yourself a data set where the details of the goals are not included and where you haven't watched the game or heard the result. Now re-construct what you think happened based on the data review.
Get that right, and I'll be impressed.
OK. Popcorn time.
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lordromford said:Very interesting.
I would say though, that comparing Alli‘s data with Carey’s at the beginning of the video doesn’t quite work because Alli essentially replaced Grant on the wing while Grant moved into the centre to replace Carey. If you’d compared Carey to Grant’s second half and Grant’s first half to Alli that might have been more enlightening!
Dont want to be too critical though. Some nice analysis mate!😎👍🏼Thanks mate, maybe not a fair comparison. Dave Rudd said:I'm really ambivalent about this stuff.
Data driven analysis is fine. I worked in that area for many years. But these videos feel full of retrospective data fitting. In other words, it seems as though GAO is looking for the numbers that support what he already knows or what he felt that he saw during the game. It's telling that a lot of his 'conclusions' come where he ignores some numbers if they don't support the pre-determined opinion.
There are also lots of simplifications. Lloyd Jones ... identified as 'top defender' even though (or because?) he made no tackles. Lindsay and Clarke of Preston had total scores equal to Jones, but ... heaven forbid ... they had to make tackles to do so. I also think that Leaburn featured quite highly in GAO's 'top defender' list, but he wasn't mentioned.
On that note, I though that Leaburn had an outstanding game. Again ... not mentioned.
These post-match analyses are great fun and I know that many people enjoy them. But let's not get too focussed on what the pseudo-science might be telling us. I came away from the game knowing that Alli had made the difference, that Grant's mobility was crucial and that McNamara and Leaburn did us proud. But that view wasn't based on any numbers.
Here's my challenge, @GAO. Get yourself a data set where the details of the goals are not included and where you haven't watched the game or heard the result. Now re-construct what you think happened based on the data review.
Get that right, and I'll be impressed.
OK. Popcorn time.Thanks mate! This is just one analysis out of many we could look at.
I worked as a scientist for over 15 years, analyzing data and drawing conclusions from it. I actually did my last postdoc at Harvard Med School before stepping away from academia, so digging into data challenges like this is what I've spent half my life doing.
To address your points: if you look at the video, I didn't analyze the specific action of the goal. I didn't mention Alli's assist to Grant, nor did I show any footage of the actual goal. I could exclude the details of the goal as you suggested, but I don't really see how that changes the video's conclusion or what the data highlighted. The 'Top Defender' metric is based strictly on actions made during the game—it's not an opinion. I do have a high overall opinion of Jones because he's been doing great, but that's not based on this single match alone.
Another example: right after the match, my impression was that Leaburn had an amazing game (probably the same as you felt), and he likely did. However, his name isn't showing up in the numbers for final third entries, crosses, top 10 threatening players, ball progression, or shot sequence involvement. I'm not saying he didn't play incredibly well (on my perspective); I'm just saying that's not what the data is showing.
On a final note, I watched this game (and the last three) wrangling my 1-year-old and 6-year-old while their mom was at the gym. Being 100% honest, it’s sometimes hard to keep my eyes on the screen. I certainly watched the match, but I definitely missed parts. Ultimately, what I'm trying to say is that all of my analysis is driven strictly by the post-match graphs and data, rather than relying solely on the eye test from the TV broadcast.
ps: really appreciate comments like yours as help me go through challenges when presenting data, thanks mate again, appreciated1 -
Is that a Palace shirt I see over your chair mate, not a good look...🤔0
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I knew that would raise some questions—apologies, mateeastterrace6168 said:Is that a Palace shirt I see over your chair mate, not a good look...🤔
Anyway, it's a San Lorenzo shirt. My main YouTube channel (gaodelacalle) is actually about San Lorenzo, but I fell in love with Charlton after learning about the club's history2 -
GAO said:lordromford said:Very interesting.
I would say though, that comparing Alli‘s data with Carey’s at the beginning of the video doesn’t quite work because Alli essentially replaced Grant on the wing while Grant moved into the centre to replace Carey. If you’d compared Carey to Grant’s second half and Grant’s first half to Alli that might have been more enlightening!
Dont want to be too critical though. Some nice analysis mate!😎👍🏼Thanks mate, maybe not a fair comparison. Dave Rudd said:I'm really ambivalent about this stuff.
Data driven analysis is fine. I worked in that area for many years. But these videos feel full of retrospective data fitting. In other words, it seems as though GAO is looking for the numbers that support what he already knows or what he felt that he saw during the game. It's telling that a lot of his 'conclusions' come where he ignores some numbers if they don't support the pre-determined opinion.
There are also lots of simplifications. Lloyd Jones ... identified as 'top defender' even though (or because?) he made no tackles. Lindsay and Clarke of Preston had total scores equal to Jones, but ... heaven forbid ... they had to make tackles to do so. I also think that Leaburn featured quite highly in GAO's 'top defender' list, but he wasn't mentioned.
On that note, I though that Leaburn had an outstanding game. Again ... not mentioned.
These post-match analyses are great fun and I know that many people enjoy them. But let's not get too focussed on what the pseudo-science might be telling us. I came away from the game knowing that Alli had made the difference, that Grant's mobility was crucial and that McNamara and Leaburn did us proud. But that view wasn't based on any numbers.
Here's my challenge, @GAO. Get yourself a data set where the details of the goals are not included and where you haven't watched the game or heard the result. Now re-construct what you think happened based on the data review.
Get that right, and I'll be impressed.
OK. Popcorn time.Thanks mate! This is just one analysis out of many we could look at.
I worked as a scientist for over 15 years, analyzing data and drawing conclusions from it. I actually did my last postdoc at Harvard Med School before stepping away from academia, so digging into data challenges like this is what I've spent half my life doing.
To address your points: if you look at the video, I didn't analyze the specific action of the goal. I didn't mention Alli's assist to Grant, nor did I show any footage of the actual goal. I could exclude the details of the goal as you suggested, but I don't really see how that changes the video's conclusion or what the data highlighted. The 'Top Defender' metric is based strictly on actions made during the game—it's not an opinion. I do have a high overall opinion of Jones because he's been doing great, but that's not based on this single match alone.
Another example: right after the match, my impression was that Leaburn had an amazing game (probably the same as you felt), and he likely did. However, his name isn't showing up in the numbers for final third entries, crosses, top 10 threatening players, ball progression, or shot sequence involvement. I'm not saying he didn't play incredibly well (on my perspective); I'm just saying that's not what the data is showing.
On a final note, I watched this game (and the last three) wrangling my 1-year-old and 6-year-old while their mom was at the gym. Being 100% honest, it’s sometimes hard to keep my eyes on the screen. I certainly watched the match, but I definitely missed parts. Ultimately, what I'm trying to say is that all of my analysis is driven strictly by the post-match graphs and data, rather than relying solely on the eye test from the TV broadcast.
ps: really appreciate comments like yours as help me go through challenges when presenting data, thanks mate again, appreciated
I appreciate the response ... and don't misunderstand me ... I am data-driven too. It has been a large part of my working life.
My concern with this type of approach is the classic issue of apparent causation versus simple correlation. And you even say yourself that Leaburn's clearly impressive performance was not supported by any data metrics. If so, that brings into question the value of those metrics and the correlation (or lack of correlation) between various data elements and player/team performance.
When I went through the UK FA coaching courses many years ago, we were forced at that time to follow the mantra of Charlie Hughes who was Director of Coaching at the Football Association. Hughes had studied all goals scored at previous World Cup Finals competitions and had concluded that goals were generally scored after no more than three or four consecutive touches by the attacking team. Thus, and by using flawed 'reverse correlation', he concluded that direct-style football was necessary in order to score goals (ie no more than three or four touches). Teams and coaches were then encouraged to get the ball into the opposition penalty area as quickly/directly as possible.
You see the fallacy, I'm sure.
The same risk applies nowadays to more sophisticated data metrics. Apparently lots of goals are scored from Zone 14, so get the ball into Zone 14 and you'll score more goals. Does that sound familiar?
Data analysis allows a hypothesis to be established. Then, if a rationale between cause and effect can be proposed, that's fine. But let's try to avoid falling into the trap of ... "Most people die in bed, so stay out of bed and live longer."
As a parting shot, I recommend "New evidence for the Theory of the Stork" by Thomas Höfera, Hildegard Przyrembelb and Silvia Verlegerc. Published in Paediatric and Perinatal Epidemiology 2004, 18, 88–92 (© Blackwell Publishing Ltd), the paper deals with the increase in the stork population around the city of Berlin and the increase in (non-hospital) child birth numbers.
Keep up the good work, @GAO. Such exercises are great fun and you have a mathematically-hungry audience here at Charlton Life. Just wait until March next year and you can be part of our legendary 'R-number' discussions.0
