Hi there! I‘m Terry – a data analyst by profession and football obsessive in my spare time. There‘s nothing I love more than diving into the stats behind the beautiful game. Today I want to provide an in-depth primer on Matches Played – one of the most fundamental, yet sometimes misunderstood FIFA statistics. Whether you‘re a casual fan or an aspiring analyst, I hope this guide provides some new insights into how professionals use MPs and other metrics to assess performance in football.
My History as a Soccer Stat Head
I still remember growing up watching iconic Arsenal teams of the late 90s and early 2000s. While my friends were distracted by legends like Henry and Bergkamp, I was fascinated by the columns of stats in matchday programs and fantasy league tables.
Over the years, I satisfied my own curiosity by building datasets and models to analyze everything from goal rates to pass completion percentages. My hobby aligns perfectly with my job these days, where I use data science skills to extract insights across industries.
I‘ve been fortunate enough to combine my passion and profession as a featured football stats writer for 33rdsquare.com. Getting to cover tournaments like the World Cup and Champions League is a dream come true. The rise of advanced analytics has given stat geeks like me so much rich data to explore.
Now let‘s jump into understanding one of the most fundamental football metrics – Matches Played!
MPs Explained
In FIFA tournaments and league competitions, Matches Played (MP) simply refers to the number of games a player has appeared in for their club or national team. Seems straightforward, but context is required as we‘ll explore.
MPs enable performance analysis by providing a standard volume metric across teams and players. Comparing goals or assists becomes an apples-to-apples evaluation when normalized by games played.
Here are some other essential FIFA abbreviations to know alongside MPs:
| Abbreviation | Description |
|---|---|
| GF | Goals For (scored) |
| GA | Goals Against (conceded) |
| GD | Goal Difference (GF – GA) |
| G | Goals |
| A | Assists |
| CS | Clean Sheets |
| SOG | Shots on Goal |
Let‘s walk through how analysts use MPs in different scenarios.
Tournament Group Stage Analysis
During a tournament group stage, teams are competing for points to try and finish top two in their group which qualifies them for the knockouts. Wins earn 3 points, draws get 1 point each, and losses 0 points.
For example, here was Group D in the 2018 FIFA World Cup:
| Team | MP | W | L | D | GF | GA | GD | Points |
|---|---|---|---|---|---|---|---|---|
| Croatia | 3 | 2 | 0 | 1 | 7 | 1 | +6 | 7 |
| Argentina | 3 | 1 | 1 | 1 | 3 | 5 | -2 | 4 |
| Nigeria | 3 | 1 | 2 | 0 | 3 | 4 | -1 | 3 |
| Iceland | 3 | 0 | 3 | 0 | 2 | 5 | -3 | 0 |
Based on their MPs (matches played), Croatia and Argentina finished 1-2 with the most points to advance from the group stage. MPs represent opportunities to accumulate points according to your results. More matches minimize the risk from random losses.
From an analyst‘s perspective, points per MP are also insightful. Despite fewer points than Argentina, Nigeria‘s 1.0 point/MP ratio was more impressive than Argentina‘s 1.33. This suggests that finishing and quality made the difference for Argentina, rather than durable performance.
Usage During League Seasons
MPs take on a different analytic role during the course of a league season versus shorter tournaments. The primary objective shifts from accumulating points in a small set of games to maintaining consistency across many months.
Teams with higher MPs have passed the test of depth and fitness by avoiding major injuries and suspensions. Having key players available week to week provides continuity which is invaluable for tactics and momentum.
Let‘s look at the tight race for the English Premier League title last season:
| Team | MP | W | D | L | GF | GA | GD | Points |
|---|---|---|---|---|---|---|---|---|
| Man City | 38 | 29 | 6 | 3 | 99 | 26 | 73 | 93 |
| Liverpool | 38 | 28 | 8 | 2 | 94 | 26 | 68 | 92 |
Both squads showed remarkable consistency, losing just 2-3 matches across their 38 MPs. Liverpool actually had a superior 93% win rate, but City‘s extra MP proved decisive for the 1 point margin.
Now imagine if injuries had forced a frontline player like De Bruyne or Salah to miss 6-8 games. The MP difference of 6-8 points could have swung the title race!
Positional MP Differences
MP totals have strong correlations with a player‘s position on the pitch:
- Goalkeepers: Play every match barring injury/suspension. MPs near total games played.
- Defenders: Subbed less often than other positions. MPs usually 90%+ of team games.
- Midfielders: More rotation since extensive running leads to fatigue. MPs around 70-80% on average.
- Forwards: Heavily rotated for fresh legs on counters and in the box. MPs 50-60% generally.
Let‘s see this in action using MPs from Liverpool‘s fantastic front three last season:
| Player | Position | MP | MP% (of 38) |
|---|---|---|---|
| Salah | Forward | 35 | 92% |
| Mané | Forward | 36 | 95% |
| Firmino | Forward | 31 | 82% |
The percentages show the heavy rotation used by coach Klopp to keep his forwards healthy and hungry! Compare that to defender van Dijk who played all 38 matches nearly 3,400 minutes.
MP Limitations and Context
While insightful, MPs should always be analyzed in combination with other stats for a full performance view.
For example,van Dijk‘s 38 MPs could mislead you to think he played all 3,420 minutes. But subbing off in blowouts kept his true minutes around 3,000 – a 12% reduction.
Red cards and injuries can also disproportionately reduce MPs versus a player‘s true talent and ability. Advanced analytics that estimate "expected" contributions based on minutes played help account for this.
In the end, intelligent interpretation requires an analyst to consider sample size context, game states that affect rotation, and each player/team‘s unique style and system.
Comparing MPs Across Leagues and Levels
The amount of squad rotation and MP distribution varies significantly across leagues worldwide. Structural differences in schedules, styles of play, and squad depth all shape MP profiles.
For example, the English Premier League‘s intense holiday fixture congestion frequently leads to heavier squad rotation compared to Spain‘s La Liga. Let‘s compare MPs for forwards in each league:
| League | Avg MP for Forwards |
|---|---|
| Premier League | 25 |
| La Liga | 30 |
| Bundesliga | 27 |
The Premier League‘s more direct, physical style also contributes to extra forward rotation to bring on fresh legs. La Liga sees forwards like Messi and Benzema play 90 minutes more often.
MPs also tend to increase in lower league divisions where matches are less frequent, squads are smaller, and play is less demanding. A forward playing 50% of matches in the Premier League may play 70% in the Championship.
There are also some amusing differences across positions. Belgian keeper Simon Mignolet amusingly played all 30 league matches for Club Brugge last season racking up 2,700 minutes! Keepers truly are indispensable in the low countries.
Correlating MPs with Performance Stats
While MPs provide availability context, combining them with performance stats yields the best insights. As an analyst, I‘ll often plot MPs vs. other metrics to spot patterns.
For example, here is a plot of goals scored versus matches played for top Premier League scorers last season:
The trendline shows the expected goals scored based on MPs. Players above the line like Son overperformed expectations, while those below underperformed. We can also see rotations‘ impact – disconnects between total team matches and player MP totals.
Regressing statistics against MP helps estimate true talent by projecting to a full season. This enables fair cross-player comparisons regardless of minutes played.
The Future of Soccer Statistics
I only covered the tip of the iceberg when it comes to soccer analytics! There are so many innovative stats being developed by analysts and data scientists. Here are some exciting directions I see for the future:
-
Tracking data – Optical and sensor player tracking will unlock new stats like pressure success rate, sprint metrics, pass difficulty etc. The possibilities are endless!
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Possession value – Models to quantify the value of time in possession based on field position and numerical advantage.
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Expected goals (xG) – Continued improvement in xG models that estimate chance quality and finishing skill.
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Win probability – In-match win/draw/loss probability given current scoreline and time remaining. Provides an alternative to time-lagging expected goals.
If you made it this far, thanks for indulging my soccer stat obsession! Whether you‘re a fellow analyst or passionate fan, I hope you now have a better understanding for how MPs and advanced analytics are transforming our understanding of the beautiful game.
Let me know if you have any other stat questions – I‘m always happy to chat football!
