Luke Bornn
@LukeBornn
Co-Founder and Chief Scientist, @ZelusAnalytics. Formerly @SacramentoKings, @SFU, @ASRomaEN, @Harvard
🎧 Episode #69 of TGG Pod is with @LukeBornn 📊 Analytics pioneer 🏀⚽️Head of Data with @SacramentoKings & @ASRomaEN 🏆 Key in @ToulouseFC promotion + Cup win 🕴️Founder @ZelusAnalytics, now @Teamworks Intelligence 🥇 1st person to return on TGG Pod! 🔗podcasts.apple.com/gb/podcast/luk…
People usually point to collection bias (events never "exactly on the line") and rounding, but there's more. Tagging locations on a 105x68 pitch is really hard: 2-4m errors are normal. But lines provide a reference point, so tagged locations become much more accurate around them.
I was looking at the Premier League event data and noticed that lines are almost visible to naked eye. I think I have seen someone mentioning this before, but it should be a data entry issue, right? No reason for players to avoid lines. Can't find the reference for it.
Sports scientists -- Here's evidence that the scientific literature may be grossly over-estimating the value of acute:chronic workload ratios in predicting injuries: Talk: youtube.com/watch?v=TIRINm… Paper: lukebornn.com/papers/bornn_s… Code: github.com/lukebornn/acut…
Decided in the fall that this will be my last year authoring papers at Sloan. As such, this thread is a great (and complete) resource for the 18 papers we’ve authored there since 2014. SSAC has been really good to me and my students — v thankful for doors opened and friends made!
My lab has had 11 Sloan papers over the last 5 years: '14: EPV '15: Counterpoints, Move or Die '16: Pressing Game, Court Realty '17: Possession Sketches, Scorekeeper Bias '18: Open Spaces, NFL Injury, NBA Replay, Deep Learning Trajectories here's a summary thread of them all: