Player Analysis; Can we account for pressure and how it affects players?

We’ve all heard the cliché, he does it when it matters, she’s always good in the late stages when the game is in the line. They perform when the game is there to be won. However, all of these anecdotes always pose the question can we measure clutch? Many consider analytics cold, nothing but facts with no ability to consider emotions. However, we can build our analysis models to take the circumstances into account. We can consider the factors that could affect a player physically or mentally and alter our analysis models to reflect these. In this piece, we’ll reflect on player ratings and how they can be shaped to take into account a player's circumstances.

Stephen Cluxton kicking a score in what was possibly the most pressure faced by a footballer in the last 20 years

Firstly before we sculpt our player ratings to take into account the player’s circumstances we give a short overview of creating a player rating. For this article, we will take a straightforward approach. Each KPI includes an event and outcomes. For example, a shot is an event and possible outcomes include a goal, point, wide, dropped short. To create player ratings we can assign a nominal value to each outcome. So, staying with the shooting example a score might be worth a plus 2 for a player, whereas a ball dropped short might be minus 1. Each outcome can have a value set by the performance analyst and management. The values are created to reflect what the team values and thus rewards the type of play they want to see. At the end of the game, you may have an overall player rating which measures whether the player has had a positive, negative or neutral contribution to the game.

Can time impact a player? Is a score in the last 5 minutes after 55 minutes of running more impressive than one taken in the first 10 minutes? Is being turned over in stoppage time more detrimental as opposed to it happening early in the second half? If you value players being able to contribute during certain stages of the game then we can add a multiplier to our player rating to reflect this.

For example, if we value how we finish games then KPI outcome value in the final 20 minutes might get multiplied by 1.2, and by 1.5 in the closing 10 minutes. On the flip side, your team’s strategy may be focused on a fast start. As a result, you may want to add a multiplier to the opening 10 minutes where the outcome values are multiplied. Regardless of your opinion on what portion is most valuable you can create a player rating that takes this into account.

A second way of measuring pressure or other external factors is the scoreline. We associate high-pressure games with tight score lines. When the game is in the balance and 1 score or one action can potentially transform the result. We can assume that this is when the pressure on players is at its peak. As with the match time, we can use a multiplier that will highlight those performing under pressure. For example, if a game is within 3 points you might use a multiplier of 1.5. If the game is within 1 point your multiplier might be 1.8. On the flip side if you’re leading or losing by a substantial amount for example 10 points then your multiplier might be .8 as the outcomes at this point in the game are less valuable.

Another Scenario where you might add a multiplier is when your team is competing in Knockout games. These games have more on the line than group stages and league games and inherently bring more pressure onto the players. As a result, you may use a multiplier of 1.2 as the game’s baseline and add any additional multipliers for the time or scoreline.

If you have the resources available to use GPS units you would be able to apply these multipliers to events that occurred once players cross a certain distance covered or if their fatigue had reached a certain level. This personalised approach would help you apply the multipliers to different players at different times based on their position and fitness levels.

All in all, Stats don’t have to be these cold numbers that don’t reflect the circumstances players face. We can create player ratings that will measure not only their performance but their performance under pressure. In doing so we can bust any myths of players being a big game or big moment player based on anecdotal events. We can in fact measure which players step up under pressure and make match-winning contributions when the games are there to be won.