The recently concluded KPL Footballer of The Year Award (FOYA) brought out the place statistics that Kenyan football has avoided for long. On social media, people started to bring up figures to prove why a certain player deserved an award and not the other. Others rubbished statistics as a basis of judging performance in sports. Statistics experts will tell you that figures do not lie, and you can explain everything using numbers. So why has KPL not taken data analysis seriously?
I always consult my friend Mike Kirwa whenever I want some data to back up an article. For long he was the only guy interested with the numbers from KPL. There is always a bias on whatever someone sees or thinks he has seen. The opinions on a player or team’s performance will be as diverse as the eyes that saw and faculties that synthesized it.
Michael Lewis in his book Money Ball gives an insight on what statistics did to Oakland Mets Baseball club. Billy Beane as CEO managed a club of undervalued players and executives who had been rejected by the big league clubs. Where conventional scouts looked for players abilities in runs, throw, field, hit and hit with power, Bill looked deeply at a player’s on base percentage. The computer did the job and they reached the playoffs with a minimal budget. Baseball is rather an individual sport played as a team unlike football which is pure team sport played by individuals so you cannot have full comparison.
To extrapolate it to football and KPL in particular let us look at Atletico Madrid last season and Nairobi City Stars. It is like saying Nairobi City Stars with its financial constraints being in Sofapaka’s position this season, going for a double up to the last day of the season. Atletico Madrid won La Liga where Real Madrid with a wage bill of 220 million euros, Barcelona of 195 million and Atletico just 72 million. You learn that there is something even money can’t buy.
Atletico won La Liga with a ball possession average of below 50 percent. A new paradigm in a world that believes superior ball possession wins matches, the team was lethal off the ball. It is always prudent to note the exceptions when evoking stats. The problem is the emotive nature of football, which may blind us to interpret the statistics with a bias.
First, individual statistics in football can be very subjective without the aid of digital equipment. It is easy to gauge a striker from goals scored, which is obvious. Danny Sserunkuma scored more goals than every other KPL player in 2014. If KPL wants to substantiate why he did not make it to Player of the Year list, they can go further and give us figures of his contribution to the games in 2014, which we are yet to invest in the means of collecting..
When looking at midfielders, it gets a little easier due to their role in football. They interlink play, covering defense then initiating and assisting in attack. It is always easier to get the percentage of complete passes a player made. One can also get the number of passes directly linked to a goal (assists) and the number of passes a midfielder intercepted from opponents.
To get a players objective output in every game and overage in a season to generate a statistical report to back annual awards will need extra input which is easier said than done. Ekaliani Ndolo’s output while playing with Humphrey Mieno was lower but went higher after Mieno left for Leopards. Do you heap the negatives on Ndolo or attribute it to partnerships on the pitch. The other easy parameter is distance covered, which may not be representative of overall match influence but a good indicator of a players fitness level.
When looking at defenders, a team can concede many goals but one defender can be outstanding. His partners may not have been good enough to offer cover. When a defense plays with a specific holding midfielder, they can perform better than when playing with another. Do you deny David “Calabar” Owino an award because Geoffrey Kizito and Collins Okoth did not offer adequate cover ? These are the limitations of data in football especially in regard to individual player’s output that must be looked into when justifying a player’s performance based on statistics.
The data KPL and clubs keeps cannot add any value to local football. These comes in form of scores, yellow cards, red cards, number of matches players play and may be match attendance. To be sincere, these are mundane figures that anybody can take, but to get an edge, KPL and clubs must invest in data collection and analysis.
In the beginning of 2012-13 season Manchester City's data department analyzed about 400 corners in several European top leagues over seasons, and concluded that the most dangerous corner is the inswinger: the ball that swings in towards goal. Roberto Manciny believed in the outswinger.
Mancini's assistant David Platt went to chat with the data analysts, and they noticed that City had begun taking inswinging corners. That season City scored 15 goals from corners and won the league, the most in the English Premier League. This is how statistics can influence a team performance. Statistics works well for teams when it boils down to individuals, then one must be willing to wear out his fingers and brain cells too.
There are new apps and software that have simplified football data collection and can be acquired at affordable rates. With the uproar from KPL –FOYA Awards, it is time we looked at ways of putting the numbers behind the action on the pitch.