Time to define “analytics” for nba fans. At least how we do it. 1. Team Analytics – style of play that optimizes your PPP 2. Player Analytics – The probability that a player is able to contribute to maximizing the team PPP— Mark Cuban (@mcuban) June 20, 2019
Why is this tweet so important? This tweet is so important because Mark Cuban spills here the most important secret in data. For those who don’t know who Mark Cuban is, Mark Cuban is the owner of the Dallas Mavericks in the NBA. For football, you need to ignore all matters related to basketball in this tweet and only focus on the last sentence:
Player Analytics = the probability that a player it able to contribute to the team.Mark Cuban
That is all that player analytics is. And the probability that a player is able to contribute to the team is a number between 0% and 100%. The probability that a player is able to contribute is NOT:
- The number of Progressive Passes
- xG + aG
- Skill score
- A performance score
- Whether his performance puts him in the top 1%
- Or whatever statistic you can think off
All of those are important after you have established that it is LIKELY that a player is able to contribute to the team. Once you have done that, you can zoom in and look at what kind of player he is and whether he fits into the team, has the same philosophy of play etcetera. If you zoom in at players who are UNLIKELY to be able to contribute to the team, you at best waste precious time and at worst all the data and reports about that player make him look as if it is a good idea to hire him even though it is UNLIKELY that he will be able to contribute to the team.
What we do, is calculate for you what the probability is that a player is able to contribute to your team. When you hire FBM to find players, you get a single number between 0% and 100% for every player we report on.
The answer to the most important question differs for each team
Of course, it is highly likely that Messi will be able to contribute to any team. But in reality we almost never deal with Messi-like players. That means that the correct answer to this most important question differs from team to team. A player that is likely to be able to contribute to team X might be unlikely to be able to contribute to team Y, especially if that team is playing in a higher league.
So what we do is calculate what the probability is that a specific player is able to contribute to your team taking into regards your team. While we have average probabilities to help us search for the right players, once we have found them we make sure that we take your team into account to come up with the right probability that a player is able to contribute to your team.
How we proof that our probability estimation is the correct one
It is not easy to proof that our probability estimations are the correct ones while building up our track record. So far we have helped Heracles hire Dalmau, a young Spanish striker, who then became the #3 top scorer in the Eredivisie in his first season at Heracles. In May 2018 we calculated that he would be worth 1.75 million euro to Heracles in May 2019 and Heracles almost got that exact number transferring Dalmau to FC Utrecht if you take into account the transfer of Dessers from FC Utrecht to Heracles at the same time.
More importantly we have a number of correlations between FBM statistics and important results:
- 50% correlation between our attack score and the goals striker score in the next season.
- 80% correlation between our FBM team score and league rank.
- 90% correlation between our FBM team score and numbers of points scored in the league.
- 88% correlation between our FBM team score and the value of teams.
We’re very happy to demonstrate to you how we find players and calculate the probability that these players are able to contribute to your team. To arrange a demonstration email us at firstname.lastname@example.org