“Analytics don’t work!”
We have crossed a point of no return…
Analytics WILL play an more important role in the future of basketball. Also internationally. Also in Europe. There’s no way back, it’s inevitable. And it will be crucial in closing the remaining gap between international basketball and the NBA. As a coaching staff, you should better be prepared if you want to stay ahead of your opponents!
Non-believers like Charles Barkley, argue that analytics don’t make you win games. I’ll accept the challenge of convincing you of the opposite with my upcoming content. For once and for all.
But don’t worry, I’ll never discuss analytics without the X’s and O’s. My professional coaching career and my background in mathematics allow me to combine the best of both worlds.
“Which analytics insight has the biggest impact on winning games?”
That’s the big question I will focus on.
Because winning games is the ultimate measure by which coaching jobs are being kept or lost. And boy, few job markets are so volatile than the European coaching market!
The 4 factors on offense & defense are a good starting point for anyone who wants to understand the game from an analytical point of view (*). So a logic start for my quest is which of the “four factors” has the clearest correlation to winning games. From there on, we’ll start slicing in deeper.
For this case study I will focus on the Euroleague ’19-’20 season (which has been stopped due to Covid-19 after 28 gamedays).
In the diagram below you see the WIN% versus the effective field goal percentage. This eFG% is an adjusted field goal percentage that takes into account the fact that a three-pointer is worth an extra point:
So teams win more when they effectively put the ball in the basket. No surprise, right? But winning a game is more than shooting the ball. We didn’t put defense, rebounding or turnovers into the equation yet.
I don’t expect necessarily a linear relationship, but still I added a trendline to the diagram for a better understanding.
Teams above the trend line are teams that win more than one would expect based upon their eFG%. For example, Barçelona is the 2nd best rebounding team in the competition. ALBA Berlin has the worst defensive rating which makes them win clearly fewer games than predicted by the eFG%.
None of the other analytical factors shows such a strong correlation with the WIN%. So it’s an easy choice to slice deeper into the eFG% in order to find the ultimate cypher that makes us win games.
After the weekend I’ll come back to you. I’ll identify THE type of shot that garantees a high eFG% (and thus wins). Not just a very specific spot on the court, but from a very specific situation. And I won’t stop there. I’ll connect it to the X’s and O’s on how to create those shots!
(*) If you want to know more about the Four Factors, download my FREE e-book “How to win more … by playing slower“.
Very interesting and indeed the future. Looking forward to your insights Pascal!
Been digging into Machine Learning with Tensorflow myself using this article, maybe you know it allready but wanted to share since it’s a good starter to demonstrate the hughe potential of analytics in basketball: https://fastbreakdata.com/classifying-the-modern-nba-player-with-machine-learning-539da03bb824#.dutxn9ia9
Regards
Jeff