Professional sports teams today deploy sports analytics methods as a top strategy for winning games. It has thus prompted managers to crystallize the role of analytics in other related activities within sports teams.
Michael Lewis, in his 2003 book money-back described ‘theory of Analytics’ as the secret to success, player, and team evaluation. Lewis states mat scoring runs, and possession was a product of specific analytics. Further results of Lewis findings showed that players fit into teams with data, and not body composition prototypes. Analytics-driven teams have increased today; trickling down to fans and journalists consuming analytics content of games. It helps these readers predict the possible outcome of matches and player performances.
Frequency Distribution shows the number of wins, loses or draws a team can have in a season. Helping to predict a team’s performance in specific games, time of the season and what causes variations in their performances.
Mean and Median determines a player’s average and mid-season performance. The use of player rating in games predicts how a player would perform against individual teams, and how that performance can be improved. For example, NFL quarterback Tom Brady had a mean of 251 yards in his 15 starts in the 2011 season. Coach Belichick would use this data to help Brady improve his yards and go on to win championships.
Probability outplays the concept of outcome in sports. It answers questions to particular events; how many times will Eden Hazard be fouled when he plays from the right flank to when he is playing from the center midfield. The coach deploys this analytics and field’s Hazard to the team’s advantage.
Regression has also been used to monitor player contributions in their specific roles. In 2011, the La Liga findings showed that Cristiano Ronaldo contributed more offensively than any other player in the league. This offensive contribution measures why he is a valuable player and makes a considerable contribution to his team.
Role of Analytics in Sports Teams
In the 2007 UEFA Champions League, analytics discovered that Manchester United goalkeeper had challenges making saves at the low left corner of the goal post. A lot of opposing players exploited this information.
Deployed in Tennis, analytics allows a player to determine his performance in a game. Such players ask questions which includes what my serve percentage was? How many times did I make unforced errors or where on the court are my best performances? Players are therefore able to examine the heart of their games, identify problems and make improvements.
For Example, Rafael Nadal has been described as the King of Clay in tennis, with about 10 French Open titles. However, data has shown his advantage on clay is entirely because of his opponents’ serves. Findings showed no significant difference between his serves on clay and other surfaces. Therefore Nadal can be defeated on clay if his opponents improve their serves.
How Teams Have Leveraged Analytics
Oakland manager Billy Beane shook up the player recruitment of his team when they were underperforming. Billy emphasised the use of stats as a lead for his scouts, bringing in players based on analytics.
The New generation of football analytics is making attempts to predict outcomes, which has been evaluated by both managers and bookmakers, Rory Campbell, a sports risk analyst, advised Liverpool to buy their foremost attacking trio.
Firminio, Sadio Mane, and MO Salah ail came in three successive seasons, giving the club players that can play effectively in all attacking positions, which has made Liverpool potential European Champions. Predictive analytics was employed for Sadio Mane and Salah; with both players performing optimally when they are allowed freedom anywhere in the attack.
Injury Risk Analysis
The cost of injuries to Premier League clubs stands at $336 Million per year and 16% player loss time to the club. It means the average team will lose players to injury to at least ten games. We have seen how injuries to players have affected the performance of clubs at particular times. The damage sustained by Mo Salah at the Final of the UEFA Champions League dealt a big blow to Liverpool. It reduced the attacking threat of the premier league side.
Kitman labs, a predictive analytics company, developed “an athlete optimization System” to help clubs reduce injury risks. Kitman Labs analyzed data on hamstring injuries to determine the risk factors. The findings were used to work with Houston Dynamo Team, a Major League Soccer-based club. The seasonal in-game player injury was decreased from 11 to 4 and player unavailability reduced by 88%.
The use of Analytics has helped teams to build an efficient structure on the field, and it will help improve performance if applied carefully as sporting teams are beginning to recognize that analytics can provide inestimable details to improving sporting performance.
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