{"id":2150,"date":"2020-06-15T15:38:18","date_gmt":"2020-06-15T15:38:18","guid":{"rendered":"https:\/\/mrktinsights.com\/?p=2150"},"modified":"2020-06-15T15:38:21","modified_gmt":"2020-06-15T15:38:21","slug":"forecasting-and-anticipating-minutes","status":"publish","type":"post","link":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/","title":{"rendered":"Forecasting and Anticipating Minutes"},"content":{"rendered":"\n

How many Premier League minutes do you think Mohamed Salah will play next season?<\/p>\n\n\n\n

It may seem a strange question, but accurately anticipating how many minutes players will play in future seasons is absolutely key to building successful squads.<\/p>\n\n\n\n

Salah generally starts every game, has a good fitness record, and is young enough that you wouldn’t expect a sudden decline.<\/p>\n\n\n\n

We know 38 x 90 = 3420 minutes (excluding injury time) is the maximum possible for any Premier League player so let us say that 2500+ minutes would be a good guess for 2020\/2021.<\/p>\n\n\n\n

What about for 2021\/22? Or 22\/23? The certainty decreases. We lack information about future form and fitness, maybe Liverpool have a youngster who is better than 30 year old Salah or maybe Real Madrid make an offer that can’t be refused.<\/p>\n\n\n\n

The further ahead we try and anticipate the more difficult it is to have certainty.<\/strong><\/p>\n\n\n\n

What is even trickier is predicting future growth of players. <\/p>\n\n\n\n

Sticking with Liverpool, how many Premier League minutes do we expect from Curtis Jones next season?<\/p>\n\n\n\n

He is an excellent young player but Liverpool have established players ahead of him and very specific tactical requirements. Maybe 300 minutes?<\/p>\n\n\n\n

What about in 2022\/23? Do we anticipate 2500 minutes and him as a first-team regular?<\/p>\n\n\n\n

This series of articles will show why forecasting matters, and look at the logical steps clubs can take to plan their recruitment effectively.<\/strong><\/p>\n\n\n\n

For as we will see there is a very high correlation between coherently assembled squads and success.<\/p>\n\n\n\n

Quality not quantity<\/strong><\/p>\n\n\n\n

The below graphs show the number of players used for >100 minutes this season in every club from the Premier League to League Two.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

In each step we drop the average number of players used per team increases.<\/p>\n\n\n\n

Premier League = 21.8 players used<\/p>\n\n\n\n

Championship = 24.5 players used<\/p>\n\n\n\n

League One = 25.5 players used<\/p>\n\n\n\n

League Two = 26.1 players used<\/p>\n\n\n\n

The correlation is interesting too between the number of players used and the points gained.<\/p>\n\n\n\n

The X axis shows the number of players used and the Y axis the points gained in 2019\/20 to date.<\/p>\n\n\n\n

In the Premier League data we see a slight negative correlation between the number of players used (for >100 minutes) and the number of points gained.<\/p>\n\n\n\n

With an R value of -0.27 the relationship isn’t that strong.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

In the Championship we see a medium-strength negative correlation. This time R-0.66<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

League One comes in a strong negative correlation of R-0.797<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

And League Two also had a moderate negative correlation of R-0.57<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

In summary there is a good chance that the more players a team uses over the course of a season the fewer points they will gather.<\/strong><\/p>\n\n\n\n

But as everyone who has ever mentioned correlation on the internet is always told; correlation does not equal causation.<\/strong><\/p>\n\n\n\n

In other words we need to look at why might a team change frequently?<\/p>\n\n\n\n

Injuries, financial problems, losing lots of games, managerial changes, different systems.<\/p>\n\n\n\n

So perhaps we’d be better saying that the problem is instability.<\/p>\n\n\n\n

Stable teams do better. They have smaller, tighter, and more coherently formed squads.<\/p>\n\n\n\n

It isn’t a surprise to see that the teams with the fewest used players in the Premier League are Burnley, Sheffield United, Watford, Wolves, and Brighton. These are fairly coherent squads who are the leagues overachievers financially.<\/p>\n\n\n\n

We can take this further and look at the core players, those squads members playing at least 2000, roughly half a season, of game time each year.<\/p>\n\n\n\n

This time we want to compare the size of the core (defined as those players with over 2000 league minutes played) with the number of points won in the season.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

The Premier League season (within our dataset) that correlates most strongly with the theory that playing a stable core lineup leads to good results is the 2015\/16 season. Leicester\u2019s famous title winning team was basically unchanged each week. <\/p>\n\n\n\n

Even so in the Premier League core stability is only moderately correlated to points won. 0.44 in this cherry-picked season and usually around 0.25.<\/p>\n\n\n\n

In the 2019\/20 season to date we see a 0.26 correlation. Perhaps if we were to list which teams are most coherently assembled for the money available to them we may see a pattern though with Wolves, Leicester and Sheffield United the top three for using the same core regularly. <\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

How about the EFL divisions during 2018\/19?<\/p>\n\n\n\n

The Championship had a moderate correlation of 0.51. The relegated Premier League clubs tended to have large squads and failed to find a regular core. Birmingham\u2019s point deductions forced them into using a small squad and actually performed well.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

League One showed the lowest correlation at 0.18 but looking into the data it is obvious as to why. Luton Town topped the division with a core of 9 who played almost every week. Other high performing teams showed up well too. However both Sunderland and Charlton rotated players throughout the season and finished in the top 6. It is certainly possible to brute force the creation of a winning team by trying lots of different players but it doesn\u2019t seem to be financially prudent.\u00a0Remove the outliers and the correlation is around 0.40.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

League Two seems to be the best example of a league where a small number of key players playing regularly helps. A 0.70 correlation this time between the number of players with 2000+ played minutes and total points won.<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

Our hypothesis is that the quality of replacement players is the key to why the Premier League teams show the smallest correlation in general between points and core stability. If Sergio Aguero is injured then playing Gabriel Jesus is not too much of a drop off. However, if the League Two top scorer is out for a month his replacement may be an untested kid.\u00a0<\/p>\n\n\n\n

The lower down the leagues you go the best ability may be availability.<\/p>\n\n\n\n

And assessing availability is the key to anticipating minutes.<\/p>\n\n\n\n

And anticipated minutes is a key part of forecasting.<\/p>\n","protected":false},"excerpt":{"rendered":"

How many Premier League minutes do you think Mohamed Salah will play next season? It may seem a strange question, but accurately anticipating how many minutes players will play in future seasons is absolutely key to building successful squads. Salah generally starts every game, has a good fitness record, and is young enough that you wouldn’t expect a sudden decline. We know 38 x 90 = 3420 minutes (excluding injury time) is the maximum possible for any Premier League player so let us say that 2500+ minutes would be a good guess for 2020\/2021. What about for 2021\/22? Or 22\/23? The certainty decreases. We lack information about future form and fitness, maybe Liverpool have a youngster who is better than 30 year old Salah or maybe Real Madrid make an offer that can’t be refused. The further ahead we try and anticipate the more difficult it is to have certainty. What is even trickier is predicting future growth of players. Sticking with Liverpool, how many Premier League minutes do we expect from Curtis Jones next season? He is an excellent young player but Liverpool have established players ahead of him and very specific tactical requirements. Maybe 300 minutes? What about in 2022\/23? Do we anticipate 2500 minutes and him as a first-team regular? This series of articles will show why forecasting matters, and look at the logical steps clubs can take to plan their recruitment effectively. For as we will see there is a very high correlation between coherently assembled squads and success. Quality not quantity The below graphs show the number of players used for >100 minutes this season in every club from the Premier League to League Two. In each step we drop the average number of players used per team increases. Premier League = 21.8 players used Championship = 24.5 players used League One = 25.5 players used League Two = 26.1 players used The correlation is interesting too between the number of players used and the points gained. The X axis shows the number of players used and the Y axis the points gained in 2019\/20 to date. In the Premier League data we see a slight negative correlation between the number of players used (for >100 minutes) and the number of points gained. With an R value of -0.27 the relationship isn’t that strong. In the Championship we see a medium-strength negative correlation. This time R-0.66 League One comes in a strong negative correlation of R-0.797 And League Two also had a moderate negative correlation of R-0.57 In summary there is a good chance that the more players a team uses over the course of a season the fewer points they will gather. But as everyone who has ever mentioned correlation on the internet is always told; correlation does not equal causation. In other words we need to look at why might a team change frequently? Injuries, financial problems, losing lots of games, managerial changes, different systems. So perhaps we’d be better saying that the problem is instability. Stable teams do better. They have smaller, tighter, and more coherently formed squads. It isn’t a surprise to see that the teams with the fewest used players in the Premier League are Burnley, Sheffield United, Watford, Wolves, and Brighton. These are fairly coherent squads who are the leagues overachievers financially. We can take this further and look at the core players, those squads members playing at least 2000, roughly half a season, of game time each year. This time we want to compare the size of the core (defined as those players with over 2000 league minutes played) with the number of points won in the season. The Premier League season (within our dataset) that correlates most strongly with the theory that playing a stable core lineup leads to good results is the 2015\/16 season. Leicester\u2019s famous title winning team was basically unchanged each week.  Even so in the Premier League core stability is only moderately correlated to points won. 0.44 in this cherry-picked season and usually around 0.25. In the 2019\/20 season to date we see a 0.26 correlation. Perhaps if we were to list which teams are most coherently assembled for the money available to them we may see a pattern though with Wolves, Leicester and Sheffield United the top three for using the same core regularly.  How about the EFL divisions during 2018\/19? The Championship had a moderate correlation of 0.51. The relegated Premier League clubs tended to have large squads and failed to find a regular core. Birmingham\u2019s point deductions forced them into using a small squad and actually performed well. League One showed the lowest correlation at 0.18 but looking into the data it is obvious as to why. Luton Town topped the division with a core of 9 who played almost every week. Other high performing teams showed up well too. However both Sunderland and Charlton rotated players throughout the season and finished in the top 6. It is certainly possible to brute force the creation of a winning team by trying lots of different players but it doesn\u2019t seem to be financially prudent.\u00a0Remove the outliers and the correlation is around 0.40. League Two seems to be the best example of a league where a small number of key players playing regularly helps. A 0.70 correlation this time between the number of players with 2000+ played minutes and total points won. Our hypothesis is that the quality of replacement players is the key to why the Premier League teams show the smallest correlation in general between points and core stability. If Sergio Aguero is injured then playing Gabriel Jesus is not too much of a drop off. However, if the League Two top scorer is out for a month his replacement may be an untested kid.\u00a0 The lower down the leagues you go the best ability may be availability. And assessing availability is the key to anticipating minutes. And anticipated minutes is a key part of forecasting.<\/p>\n","protected":false},"author":2,"featured_media":2184,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_oasis_is_in_workflow":0,"_oasis_original":0,"footnotes":""},"categories":[23],"tags":[],"class_list":["post-2150","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mrkt-insights"],"yoast_head":"\nForecasting and Anticipating Minutes - MRKT Insights - Football Consultancy Services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Forecasting and Anticipating Minutes - MRKT Insights - Football Consultancy Services\" \/>\n<meta property=\"og:description\" content=\"How many Premier League minutes do you think Mohamed Salah will play next season? It may seem a strange question, but accurately anticipating how many minutes players will play in future seasons is absolutely key to building successful squads. Salah generally starts every game, has a good fitness record, and is young enough that you wouldn’t expect a sudden decline. We know 38 x 90 = 3420 minutes (excluding injury time) is the maximum possible for any Premier League player so let us say that 2500+ minutes would be a good guess for 2020\/2021. What about for 2021\/22? Or 22\/23? The certainty decreases. We lack information about future form and fitness, maybe Liverpool have a youngster who is better than 30 year old Salah or maybe Real Madrid make an offer that can’t be refused. The further ahead we try and anticipate the more difficult it is to have certainty. What is even trickier is predicting future growth of players. Sticking with Liverpool, how many Premier League minutes do we expect from Curtis Jones next season? He is an excellent young player but Liverpool have established players ahead of him and very specific tactical requirements. Maybe 300 minutes? What about in 2022\/23? Do we anticipate 2500 minutes and him as a first-team regular? This series of articles will show why forecasting matters, and look at the logical steps clubs can take to plan their recruitment effectively. For as we will see there is a very high correlation between coherently assembled squads and success. Quality not quantity The below graphs show the number of players used for >100 minutes this season in every club from the Premier League to League Two. In each step we drop the average number of players used per team increases. Premier League = 21.8 players used Championship = 24.5 players used League One = 25.5 players used League Two = 26.1 players used The correlation is interesting too between the number of players used and the points gained. The X axis shows the number of players used and the Y axis the points gained in 2019\/20 to date. In the Premier League data we see a slight negative correlation between the number of players used (for >100 minutes) and the number of points gained. With an R value of -0.27 the relationship isn’t that strong. In the Championship we see a medium-strength negative correlation. This time R-0.66 League One comes in a strong negative correlation of R-0.797 And League Two also had a moderate negative correlation of R-0.57 In summary there is a good chance that the more players a team uses over the course of a season the fewer points they will gather. But as everyone who has ever mentioned correlation on the internet is always told; correlation does not equal causation. In other words we need to look at why might a team change frequently? Injuries, financial problems, losing lots of games, managerial changes, different systems. So perhaps we’d be better saying that the problem is instability. Stable teams do better. They have smaller, tighter, and more coherently formed squads. It isn’t a surprise to see that the teams with the fewest used players in the Premier League are Burnley, Sheffield United, Watford, Wolves, and Brighton. These are fairly coherent squads who are the leagues overachievers financially. We can take this further and look at the core players, those squads members playing at least 2000, roughly half a season, of game time each year. This time we want to compare the size of the core (defined as those players with over 2000 league minutes played) with the number of points won in the season. The Premier League season (within our dataset) that correlates most strongly with the theory that playing a stable core lineup leads to good results is the 2015\/16 season. Leicester\u2019s famous title winning team was basically unchanged each week.  Even so in the Premier League core stability is only moderately correlated to points won. 0.44 in this cherry-picked season and usually around 0.25. In the 2019\/20 season to date we see a 0.26 correlation. Perhaps if we were to list which teams are most coherently assembled for the money available to them we may see a pattern though with Wolves, Leicester and Sheffield United the top three for using the same core regularly.  How about the EFL divisions during 2018\/19? The Championship had a moderate correlation of 0.51. The relegated Premier League clubs tended to have large squads and failed to find a regular core. Birmingham\u2019s point deductions forced them into using a small squad and actually performed well. League One showed the lowest correlation at 0.18 but looking into the data it is obvious as to why. Luton Town topped the division with a core of 9 who played almost every week. Other high performing teams showed up well too. However both Sunderland and Charlton rotated players throughout the season and finished in the top 6. It is certainly possible to brute force the creation of a winning team by trying lots of different players but it doesn\u2019t seem to be financially prudent.\u00a0Remove the outliers and the correlation is around 0.40. League Two seems to be the best example of a league where a small number of key players playing regularly helps. A 0.70 correlation this time between the number of players with 2000+ played minutes and total points won. Our hypothesis is that the quality of replacement players is the key to why the Premier League teams show the smallest correlation in general between points and core stability. If Sergio Aguero is injured then playing Gabriel Jesus is not too much of a drop off. However, if the League Two top scorer is out for a month his replacement may be an untested kid.\u00a0 The lower down the leagues you go the best ability may be availability. And assessing availability is the key to anticipating minutes. And anticipated minutes is a key part of forecasting.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\" \/>\n<meta property=\"og:site_name\" content=\"MRKT Insights - Football Consultancy Services\" \/>\n<meta property=\"article:published_time\" content=\"2020-06-15T15:38:18+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-06-15T15:38:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mrktinsights.com\/wp-content\/uploads\/2020\/06\/Screenshot-2020-06-15-at-16.33.08.png\" \/>\n\t<meta property=\"og:image:width\" content=\"814\" \/>\n\t<meta property=\"og:image:height\" content=\"497\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Tim Keech\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insightmrkt\" \/>\n<meta name=\"twitter:site\" content=\"@insightmrkt\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tim Keech\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\"},\"author\":{\"name\":\"Tim Keech\",\"@id\":\"https:\/\/mrktinsights.com\/#\/schema\/person\/d956f2345ba372416ae11cee92a07873\"},\"headline\":\"Forecasting and Anticipating Minutes\",\"datePublished\":\"2020-06-15T15:38:18+00:00\",\"dateModified\":\"2020-06-15T15:38:21+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\"},\"wordCount\":969,\"commentCount\":2,\"publisher\":{\"@id\":\"https:\/\/mrktinsights.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/mrktinsights.com\/wp-content\/uploads\/2020\/06\/Screenshot-2020-06-15-at-16.33.08.png\",\"articleSection\":[\"MRKT Insights\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\",\"url\":\"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/\",\"name\":\"Forecasting and Anticipating Minutes - 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It may seem a strange question, but accurately anticipating how many minutes players will play in future seasons is absolutely key to building successful squads. Salah generally starts every game, has a good fitness record, and is young enough that you wouldn’t expect a sudden decline. We know 38 x 90 = 3420 minutes (excluding injury time) is the maximum possible for any Premier League player so let us say that 2500+ minutes would be a good guess for 2020\/2021. What about for 2021\/22? Or 22\/23? The certainty decreases. We lack information about future form and fitness, maybe Liverpool have a youngster who is better than 30 year old Salah or maybe Real Madrid make an offer that can’t be refused. The further ahead we try and anticipate the more difficult it is to have certainty. What is even trickier is predicting future growth of players. Sticking with Liverpool, how many Premier League minutes do we expect from Curtis Jones next season? He is an excellent young player but Liverpool have established players ahead of him and very specific tactical requirements. Maybe 300 minutes? What about in 2022\/23? Do we anticipate 2500 minutes and him as a first-team regular? This series of articles will show why forecasting matters, and look at the logical steps clubs can take to plan their recruitment effectively. For as we will see there is a very high correlation between coherently assembled squads and success. Quality not quantity The below graphs show the number of players used for >100 minutes this season in every club from the Premier League to League Two. In each step we drop the average number of players used per team increases. Premier League = 21.8 players used Championship = 24.5 players used League One = 25.5 players used League Two = 26.1 players used The correlation is interesting too between the number of players used and the points gained. The X axis shows the number of players used and the Y axis the points gained in 2019\/20 to date. In the Premier League data we see a slight negative correlation between the number of players used (for >100 minutes) and the number of points gained. With an R value of -0.27 the relationship isn’t that strong. In the Championship we see a medium-strength negative correlation. This time R-0.66 League One comes in a strong negative correlation of R-0.797 And League Two also had a moderate negative correlation of R-0.57 In summary there is a good chance that the more players a team uses over the course of a season the fewer points they will gather. But as everyone who has ever mentioned correlation on the internet is always told; correlation does not equal causation. In other words we need to look at why might a team change frequently? Injuries, financial problems, losing lots of games, managerial changes, different systems. So perhaps we’d be better saying that the problem is instability. Stable teams do better. They have smaller, tighter, and more coherently formed squads. It isn’t a surprise to see that the teams with the fewest used players in the Premier League are Burnley, Sheffield United, Watford, Wolves, and Brighton. These are fairly coherent squads who are the leagues overachievers financially. We can take this further and look at the core players, those squads members playing at least 2000, roughly half a season, of game time each year. This time we want to compare the size of the core (defined as those players with over 2000 league minutes played) with the number of points won in the season. The Premier League season (within our dataset) that correlates most strongly with the theory that playing a stable core lineup leads to good results is the 2015\/16 season. Leicester\u2019s famous title winning team was basically unchanged each week.  Even so in the Premier League core stability is only moderately correlated to points won. 0.44 in this cherry-picked season and usually around 0.25. In the 2019\/20 season to date we see a 0.26 correlation. Perhaps if we were to list which teams are most coherently assembled for the money available to them we may see a pattern though with Wolves, Leicester and Sheffield United the top three for using the same core regularly.  How about the EFL divisions during 2018\/19? The Championship had a moderate correlation of 0.51. The relegated Premier League clubs tended to have large squads and failed to find a regular core. Birmingham\u2019s point deductions forced them into using a small squad and actually performed well. League One showed the lowest correlation at 0.18 but looking into the data it is obvious as to why. Luton Town topped the division with a core of 9 who played almost every week. Other high performing teams showed up well too. However both Sunderland and Charlton rotated players throughout the season and finished in the top 6. It is certainly possible to brute force the creation of a winning team by trying lots of different players but it doesn\u2019t seem to be financially prudent.\u00a0Remove the outliers and the correlation is around 0.40. League Two seems to be the best example of a league where a small number of key players playing regularly helps. A 0.70 correlation this time between the number of players with 2000+ played minutes and total points won. Our hypothesis is that the quality of replacement players is the key to why the Premier League teams show the smallest correlation in general between points and core stability. If Sergio Aguero is injured then playing Gabriel Jesus is not too much of a drop off. However, if the League Two top scorer is out for a month his replacement may be an untested kid.\u00a0 The lower down the leagues you go the best ability may be availability. And assessing availability is the key to anticipating minutes. And anticipated minutes is a key part of forecasting.","og_url":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/","og_site_name":"MRKT Insights - Football Consultancy Services","article_published_time":"2020-06-15T15:38:18+00:00","article_modified_time":"2020-06-15T15:38:21+00:00","og_image":[{"width":814,"height":497,"url":"https:\/\/mrktinsights.com\/wp-content\/uploads\/2020\/06\/Screenshot-2020-06-15-at-16.33.08.png","type":"image\/png"}],"author":"Tim Keech","twitter_card":"summary_large_image","twitter_creator":"@insightmrkt","twitter_site":"@insightmrkt","twitter_misc":{"Written by":"Tim Keech","Estimated reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#article","isPartOf":{"@id":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/"},"author":{"name":"Tim Keech","@id":"https:\/\/mrktinsights.com\/#\/schema\/person\/d956f2345ba372416ae11cee92a07873"},"headline":"Forecasting and Anticipating Minutes","datePublished":"2020-06-15T15:38:18+00:00","dateModified":"2020-06-15T15:38:21+00:00","mainEntityOfPage":{"@id":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/"},"wordCount":969,"commentCount":2,"publisher":{"@id":"https:\/\/mrktinsights.com\/#organization"},"image":{"@id":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#primaryimage"},"thumbnailUrl":"https:\/\/mrktinsights.com\/wp-content\/uploads\/2020\/06\/Screenshot-2020-06-15-at-16.33.08.png","articleSection":["MRKT Insights"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/","url":"https:\/\/mrktinsights.com\/index.php\/2020\/06\/15\/forecasting-and-anticipating-minutes\/","name":"Forecasting and Anticipating Minutes - 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