| Further Q&As on 12:53 - Jun 12 with 1577 views | AtThePeake |
| Further Q&As on 12:05 - Jun 12 by BigDaveMyCock |
Agree with all of that. My only concern is that there is a presumption that the player, NML here, is ‘ranking’ high in the areas you mentioned. Given that he was effectively turfed out by Peterborough, there’s a chance he may not have been ranking high. His rejection by Peterborough may not have been because of personal issues but also because his performances tanked over a significant period. Which by all accounts they had. I may not be making myself clear here and apologies if I’m not but there is a premise here that the broken toys are ‘ranking’ high. However, what makes them broken toys is that they’ve not been performing. If they had they wouldn’t be broken toys. [Post edited 12 Jun 2020 12:10]
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No worries, I understand where you're coming from and I'm aware that I'm quick to defend this sort of stuff and perhaps guilty of placing too much faith in it having seen how it can work a little bit through my work.
What I would say here is that you can still be ranking high without looking like you're playing particularly well at times, if that makes sense? Or you can be ranking high in metrics that aren't particularly important or relevant to the team you're playing in.
Frustratingly, the tools we use with my work don't go down as far as League One, but I'm looking at Spurs now and looking at Serge Aurier as an example, who in my opinion without having looked at the analytics before has had another poor season and I would be happy to see sold for a fee of £10-15m.
However, his numbers for metrics like crosses from within 18 yards of the byline, crosses attempted, and switched passes are actually quite high. The issue is that these aren't really as effective when linking up with the likes of Kane and Son, who prefer the ball into feet around the edge of the box, or between defenders to run onto. Plus, his numbers are pretty poor as suspected in areas like fouls, possession lost and aerial duels won.
So even though I think Aurier is among our worst players at Spurs, if you were a side that played a 3-5-2 with a target man, then perhaps Aurier would be a perfect fit on the right side to supply crosses to your target man without having as much pressure to make a defensive contribution as he does as a right-back in a 4-3-3.
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| Further Q&As on 12:55 - Jun 12 with 1574 views | AtThePeake |
| Further Q&As on 12:39 - Jun 12 by isitme |
I understand the point you are making.
What the data will identify is the traits that a player has, their strengths, weaknesses and how best to play them. With NML it will have identified his strengths but may have also identified issues with work rate - sprints when the team is losing, distance covered between 70-90th minute compared with average etc which was preventing him from playing for Posh. Now other teams may be prepared to accept these limitations, develop tactics to negate these deficiencies or maybe a manager may think that they can coax improvements in these areas.
With regard to a 'broken toy' I am not sure that all underperforming players can be classed as a broken toy. My interpretation of what a broken toy is, is a player who is talented but due to lack of application is not reaching their potential - someone who may be viewed as being hard work/difficult/disruptive.
A player could be underperforming if they are played in a role that is unsuited to them. For example a team may play a high defensive line and a centre back may be exposed and play badly due to their lack of pace. The data may highlight this, but it also may show that they win lots of headers and have a very high tackle success rating within their own area and rarely gives away unnecessary fouls. In this case the defender may be much more suited to a team who defends deeper or plays alongside a quick defender who is asked to sweep up, whilst he attacks long balls etc. This would not make them, well in my eyes a 'broken toy'. [Post edited 12 Jun 2020 12:51]
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Apologies isitme, think we have made very similar points there but didn't see your post until I had finished mine having got lost in Tottenham stats for 20 minutes! | |
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| Further Q&As on 12:58 - Jun 12 with 1564 views | isitme |
| Further Q&As on 12:50 - Jun 12 by BartRowou |
I've scouted with Trevor Jones on Broadhurst fields in Moston. While this computer program might be able to do all that, can it reminisce about Dale's 5-0 hammering at Northampton and the brutal treatment handed out to Dave Redfern that night? |
A program cannot reminisce - no and I might be being stupid, but I do not understand the point you are trying to make.
What it would be able to do is analyse his performance that game and others and put them into context. For goalkeepers there are stats such as expected saves. So how many of these goals were his fault? Goals against is not always a good indicator of a goalkeeper's ability. How often does a keeper not make an expected save? There are lots of statistics related to cross claim success, distance scored, pass completion that can be used to analyse a goal keeper. [Post edited 12 Jun 2020 13:04]
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| Further Q&As on 13:03 - Jun 12 with 1533 views | isitme |
| Further Q&As on 12:55 - Jun 12 by AtThePeake |
Apologies isitme, think we have made very similar points there but didn't see your post until I had finished mine having got lost in Tottenham stats for 20 minutes! |
No problem. It is good as well that other people are making similar points. You obviously work in this area. I have read a lot about analytics not only in football but in other sports such as cricket, rugby and NFL. Some people swear by data, others hate it, most are somewhere inbetween.
I would say that most data raises questions, but does not give you the answer. | | | |
| Further Q&As on 13:09 - Jun 12 with 1504 views | BigDaveMyCock |
| Further Q&As on 12:39 - Jun 12 by isitme |
I understand the point you are making.
What the data will identify is the traits that a player has, their strengths, weaknesses and how best to play them. With NML it will have identified his strengths but may have also identified issues with work rate - sprints when the team is losing, distance covered between 70-90th minute compared with average etc which was preventing him from playing for Posh. Now other teams may be prepared to accept these limitations, develop tactics to negate these deficiencies or maybe a manager may think that they can coax improvements in these areas.
With regard to a 'broken toy' I am not sure that all underperforming players can be classed as a broken toy. My interpretation of what a broken toy is, is a player who is talented but due to lack of application is not reaching their potential - someone who may be viewed as being hard work/difficult/disruptive.
A player could be underperforming if they are played in a role that is unsuited to them. For example a team may play a high defensive line and a centre back may be exposed and play badly due to their lack of pace. The data may highlight this, but it also may show that they win lots of headers and have a very high tackle success rating within their own area and rarely gives away unnecessary fouls. In this case the defender may be much more suited to a team who defends deeper or plays alongside a quick defender who is asked to sweep up, whilst he attacks long balls etc. This would not make them, well in my eyes a 'broken toy'. [Post edited 12 Jun 2020 12:51]
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Thanks for the explanation.
The reason why I’m asking is that players who have played at a higher level or highly at our level often end up at Dale for two reasons. Age - they’re on their way down career wise and they can’t command the wages they once did (Alby for example). Or, they’ve experienced a significant decline in performance for whatever reason (mental, physical, family or just badly managed) and they can’t command the wage they once did. With regards to the latter, we have made a good job over recent times of getting performances out of such players. Improving their rankings. Not all but enough. As far as my limited understanding of analytics goes, it essentially measures performance over a number of variables and identifies a suitably performing player. My concern is that the players identified may be in demand and not willing to come because they’re performing and the players who would normally come are not identified because they are not performing. [Post edited 12 Jun 2020 13:12]
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| Further Q&As on 13:14 - Jun 12 with 1479 views | BartRowou |
| Further Q&As on 12:58 - Jun 12 by isitme |
A program cannot reminisce - no and I might be being stupid, but I do not understand the point you are trying to make.
What it would be able to do is analyse his performance that game and others and put them into context. For goalkeepers there are stats such as expected saves. So how many of these goals were his fault? Goals against is not always a good indicator of a goalkeeper's ability. How often does a keeper not make an expected save? There are lots of statistics related to cross claim success, distance scored, pass completion that can be used to analyse a goal keeper. [Post edited 12 Jun 2020 13:04]
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You're not stupid. I just don't share the enthusiasm for a computer program that will pick players based on statistics and probably bankrupt the club eventually, given that it comes with boardroom upheaval and the rest of it. |  |
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| Further Q&As on 13:26 - Jun 12 with 1439 views | isitme |
| Further Q&As on 13:09 - Jun 12 by BigDaveMyCock |
Thanks for the explanation.
The reason why I’m asking is that players who have played at a higher level or highly at our level often end up at Dale for two reasons. Age - they’re on their way down career wise and they can’t command the wages they once did (Alby for example). Or, they’ve experienced a significant decline in performance for whatever reason (mental, physical, family or just badly managed) and they can’t command the wage they once did. With regards to the latter, we have made a good job over recent times of getting performances out of such players. Improving their rankings. Not all but enough. As far as my limited understanding of analytics goes, it essentially measures performance over a number of variables and identifies a suitably performing player. My concern is that the players identified may be in demand and not willing to come because they’re performing and the players who would normally come are not identified because they are not performing. [Post edited 12 Jun 2020 13:12]
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I would add in another reason as to why they sign for us - pathway. They may get a lower wage but they are in the shop window and will be allowed to leave for a reasonable/derisory/acceptable fee. Somebody like O'Connell who played well last season and without Covid-19 may possibly have earned a move this summer, he still might. The other reasons are also very valid.
Again, from reading about clubs who use analytics a lot to identify transfer targets the software will list any number of players with certain attributes you specify. For example Chelsea are interested in Chilwell from Leicester. Using analytics the software can identify players who have similar traits. Diagrams can be produced to compare players in specific areas etc. Even at out level the software would be able to list a dozen, if not more players with similar traits and rank them in order of suitability. Some of these databases go down to the 8th tier of English football.
Again within the software there are options to weight more towards performance rather than traits, or set your own mix. Although you would have to define what is classed as performance - completed passes/successful dribbles etc. Performance is too arbitrary a term when dealing with analytics.
We are crying out for a quick wide player. A search could be made for all wide players who are out of contract with an expected wage demand of up to £2,000 per week. The software would then rank these based on 'perfomance' indicators chosen. | | | |
| Further Q&As on 13:31 - Jun 12 with 1414 views | Shun |
| Further Q&As on 12:53 - Jun 12 by AtThePeake |
No worries, I understand where you're coming from and I'm aware that I'm quick to defend this sort of stuff and perhaps guilty of placing too much faith in it having seen how it can work a little bit through my work.
What I would say here is that you can still be ranking high without looking like you're playing particularly well at times, if that makes sense? Or you can be ranking high in metrics that aren't particularly important or relevant to the team you're playing in.
Frustratingly, the tools we use with my work don't go down as far as League One, but I'm looking at Spurs now and looking at Serge Aurier as an example, who in my opinion without having looked at the analytics before has had another poor season and I would be happy to see sold for a fee of £10-15m.
However, his numbers for metrics like crosses from within 18 yards of the byline, crosses attempted, and switched passes are actually quite high. The issue is that these aren't really as effective when linking up with the likes of Kane and Son, who prefer the ball into feet around the edge of the box, or between defenders to run onto. Plus, his numbers are pretty poor as suspected in areas like fouls, possession lost and aerial duels won.
So even though I think Aurier is among our worst players at Spurs, if you were a side that played a 3-5-2 with a target man, then perhaps Aurier would be a perfect fit on the right side to supply crosses to your target man without having as much pressure to make a defensive contribution as he does as a right-back in a 4-3-3.
This post has been edited by an administrator |
It all sounds very interesting and I’m quite excited and intrigued about it all, without knowing much about it. My question is where does all the data come from?
Say Altman wants to find a new forward for the club, is there some kind of database out there he looks on to see all these stats? Or is it his own database that he looks on? In which case has he sent an army of scouts out to watch every forward in the lower leagues of England to see how many shots they have, how many forward runs they make, one-on-ones, etc? And that would need to be done over the course of a season I imagine rather than one individual match.
Sorry for all the questions, just curious! |  | | |
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| Further Q&As on 13:31 - Jun 12 with 1414 views | isitme |
| Further Q&As on 13:14 - Jun 12 by BartRowou |
You're not stupid. I just don't share the enthusiasm for a computer program that will pick players based on statistics and probably bankrupt the club eventually, given that it comes with boardroom upheaval and the rest of it. |
Absolutely and I completely understand the point that you are now making.
Many people are weary of analytics and probably more so about a potential takeover and how it might affect the club. The leveraged buy out of Manchester United by the Glaziers is a very interesting financial case study in The Price of Football by Kieran Maguire who runs this Twitter account https://twitter.com/KieranMaguire | | | |
| Further Q&As on 13:38 - Jun 12 with 1395 views | D_Alien |
| Further Q&As on 13:26 - Jun 12 by isitme |
I would add in another reason as to why they sign for us - pathway. They may get a lower wage but they are in the shop window and will be allowed to leave for a reasonable/derisory/acceptable fee. Somebody like O'Connell who played well last season and without Covid-19 may possibly have earned a move this summer, he still might. The other reasons are also very valid.
Again, from reading about clubs who use analytics a lot to identify transfer targets the software will list any number of players with certain attributes you specify. For example Chelsea are interested in Chilwell from Leicester. Using analytics the software can identify players who have similar traits. Diagrams can be produced to compare players in specific areas etc. Even at out level the software would be able to list a dozen, if not more players with similar traits and rank them in order of suitability. Some of these databases go down to the 8th tier of English football.
Again within the software there are options to weight more towards performance rather than traits, or set your own mix. Although you would have to define what is classed as performance - completed passes/successful dribbles etc. Performance is too arbitrary a term when dealing with analytics.
We are crying out for a quick wide player. A search could be made for all wide players who are out of contract with an expected wage demand of up to £2,000 per week. The software would then rank these based on 'perfomance' indicators chosen. |
The point about the extensive nature, the depth as it were, of databases has some bearing on my point about scouting networks
Once you cast the net too wide, the choice might become bewildering and other factors come into play. The proverbial Albanian who doesn't speak English, for instance. Would he be able to understand BBM's coaching? I have trouble understanding BBM's interviews!
Given the depth you indicate just in England, then add the rest of the UK, Ireland and perhaps countries close to the North Sea whose populations often have a grasp of English, looking much further might seem futile
Edit: i might add, of course, North America [Post edited 12 Jun 2020 13:41]
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| Further Q&As on 13:50 - Jun 12 with 1360 views | AtThePeake |
| Further Q&As on 13:31 - Jun 12 by Shun |
It all sounds very interesting and I’m quite excited and intrigued about it all, without knowing much about it. My question is where does all the data come from?
Say Altman wants to find a new forward for the club, is there some kind of database out there he looks on to see all these stats? Or is it his own database that he looks on? In which case has he sent an army of scouts out to watch every forward in the lower leagues of England to see how many shots they have, how many forward runs they make, one-on-ones, etc? And that would need to be done over the course of a season I imagine rather than one individual match.
Sorry for all the questions, just curious! |
To be honest, SmarterScout isn't a tool that I've used personally. I'm going to speak to a few of my colleagues when I get the chance to try and find out a bit more about it, all they have told me so far is that they were impressed.
As such, I'm not sure whether it collects the data itself, or it's a tool that makes it easier to tailor and utilise the data. I'd suspect it's the latter, as Opta are the main company for collecting the data and it's a mammoth task. They have scouts watching every game in every league that they cover and they have 24/7 hubs in Liverpool and Leeds amongst other places where analysts watch games live from across the globe at all times and record the data as they go. That data is then sold and used in several different ways - by media outlets, clubs themselves, video games, etc.
In fact, here's a listing for a job with Opta at the Leeds office: https://www.optasports.com/about/work-for-stats-perform/pt-uk-football-analyst/ | |
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| Further Q&As on 13:58 - Jun 12 with 1337 views | BigDaveMyCock |
| Further Q&As on 13:50 - Jun 12 by AtThePeake |
To be honest, SmarterScout isn't a tool that I've used personally. I'm going to speak to a few of my colleagues when I get the chance to try and find out a bit more about it, all they have told me so far is that they were impressed.
As such, I'm not sure whether it collects the data itself, or it's a tool that makes it easier to tailor and utilise the data. I'd suspect it's the latter, as Opta are the main company for collecting the data and it's a mammoth task. They have scouts watching every game in every league that they cover and they have 24/7 hubs in Liverpool and Leeds amongst other places where analysts watch games live from across the globe at all times and record the data as they go. That data is then sold and used in several different ways - by media outlets, clubs themselves, video games, etc.
In fact, here's a listing for a job with Opta at the Leeds office: https://www.optasports.com/about/work-for-stats-perform/pt-uk-football-analyst/ |
I may be being naive here but would a game have to be watched 22 times or by 22 analysts (not including subs) to glean the stats for each player? [Post edited 12 Jun 2020 13:59]
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| Further Q&As on 14:26 - Jun 12 with 1307 views | AtThePeake |
| Further Q&As on 13:58 - Jun 12 by BigDaveMyCock |
I may be being naive here but would a game have to be watched 22 times or by 22 analysts (not including subs) to glean the stats for each player? [Post edited 12 Jun 2020 13:59]
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Not sure how exactly that job is done, but I do seem to remember being told that Opta analysts tend to be given a handful of teams from different leagues that they are responsible for obtaining the data for. As such, maybe it's a case that the same game is watched a few times by the two team's separate analysts and they focus on 2/3 players each per watch through, or that it's watched at a slow pace to allow for the stats to be recorded for each player as it is watched? | |
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| Further Q&As on 14:31 - Jun 12 with 1297 views | TVOS1907 |
| Further Q&As on 14:26 - Jun 12 by AtThePeake |
Not sure how exactly that job is done, but I do seem to remember being told that Opta analysts tend to be given a handful of teams from different leagues that they are responsible for obtaining the data for. As such, maybe it's a case that the same game is watched a few times by the two team's separate analysts and they focus on 2/3 players each per watch through, or that it's watched at a slow pace to allow for the stats to be recorded for each player as it is watched? |
Sounds like we could save money by just calling on the methods Chaff uses to rate players for that computer game. |  |
| When I was your age, I used to enjoy the odd game of tennis. Or was it golf? |
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| Further Q&As on 14:44 - Jun 12 with 1261 views | isitme |
| Further Q&As on 13:38 - Jun 12 by D_Alien |
The point about the extensive nature, the depth as it were, of databases has some bearing on my point about scouting networks
Once you cast the net too wide, the choice might become bewildering and other factors come into play. The proverbial Albanian who doesn't speak English, for instance. Would he be able to understand BBM's coaching? I have trouble understanding BBM's interviews!
Given the depth you indicate just in England, then add the rest of the UK, Ireland and perhaps countries close to the North Sea whose populations often have a grasp of English, looking much further might seem futile
Edit: i might add, of course, North America [Post edited 12 Jun 2020 13:41]
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I would suspect that 'languages spoken/understood' would be one of the parameters that could be filtered on. Most of these systems have a league ranking systems which can make a prediction about how performances would translate in different leagues.
FC Midtjylland went heavily down the analytics route and developed from relative obscurity into one of the best Danish teams. One of their better players Tim Sparv was signed from a German Second division club. I believe Norwich also signed some players from lesser German clubs when they were in the Championship.
What a lot of models do it look for inefficiencies in transfer markets to generate value. Probably about five years ago players in the French league were under valued and therefore many clubs signed, relative bargains from this league. Price readjust and then other inefficiencies are sought.
As you rightly say some leagues feed into others. The MSL offers opportunties for declining players often from the Championship and up and coming younger players who may have been released from Premier League clubs. Scandinavia also used to be a good hunting ground for mid level Premier League/Championship clubs as many of the players speak English. Teams such as Basle and Salzburg are also seen as teams who develop players, especially from African countries. | | | |
| Further Q&As on 15:03 - Jun 12 with 1223 views | BigDaveMyCock |
| Further Q&As on 14:26 - Jun 12 by AtThePeake |
Not sure how exactly that job is done, but I do seem to remember being told that Opta analysts tend to be given a handful of teams from different leagues that they are responsible for obtaining the data for. As such, maybe it's a case that the same game is watched a few times by the two team's separate analysts and they focus on 2/3 players each per watch through, or that it's watched at a slow pace to allow for the stats to be recorded for each player as it is watched? |
Right guys, day off tomorrow. What are we doing, watching the game? Were his last words. | |
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| Further Q&As on 15:58 - Jun 12 with 1163 views | AtThePeake |
| Further Q&As on 14:44 - Jun 12 by isitme |
I would suspect that 'languages spoken/understood' would be one of the parameters that could be filtered on. Most of these systems have a league ranking systems which can make a prediction about how performances would translate in different leagues.
FC Midtjylland went heavily down the analytics route and developed from relative obscurity into one of the best Danish teams. One of their better players Tim Sparv was signed from a German Second division club. I believe Norwich also signed some players from lesser German clubs when they were in the Championship.
What a lot of models do it look for inefficiencies in transfer markets to generate value. Probably about five years ago players in the French league were under valued and therefore many clubs signed, relative bargains from this league. Price readjust and then other inefficiencies are sought.
As you rightly say some leagues feed into others. The MSL offers opportunties for declining players often from the Championship and up and coming younger players who may have been released from Premier League clubs. Scandinavia also used to be a good hunting ground for mid level Premier League/Championship clubs as many of the players speak English. Teams such as Basle and Salzburg are also seen as teams who develop players, especially from African countries. |
Great point about the French league. That definitely helped Newcastle out for a while, everyone was wondering where they had discovered these incredible, relatively cheap players like Yohan Cabaye and Hatem Ben Arfa. In reality, no analytics were needed to discover these players, who had been stars in title-winning Lille and Marseille sides respectively, but NUFC just took advantage of the Ligue 1's undervaluing of it's players. There was certainly more scouting/analytics involved when Leicester picked up Mahrez and Kante from more obscure sides a few years later, but they still took advantage of being able to get these players on the cheap due to the nature of the market in France at the time. | |
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| Further Q&As on 16:26 - Jun 12 with 1117 views | 49thseason |
Isn't it the case that the analytical information could be used to put more accurate values on our own players too? I would have thought that it would provide evidence to perhaps rebutt particularly low-ball offers for our players. Equally, knowing the market value of your squad would be useful when discussing contract renewals and salary increases.
I don't necessarily think that the introduction of cold, statistical logic into the discussions about players would be a bad thing for the Board and Manager either with a much more accurate overview on the success or otherwise of our academy and subsequent coaching successes as players progress into the 1st team squad. |  | | |
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