RT @ground_guru: 🎧 Episode #69 of TGG Pod is with @LukeBornn . 📊 Analytics pioneer.🏀⚽️Head of Data with @SacramentoKings & @ASRomaEN .🏆 Key….

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I rarely speak publicly on technical topics these days, so I really enjoyed recording an episode on Alex Andorra's Bayes podcast recently. For those with a more technical bent in the stats/analytics community, hopefully there are some useful nuggets here.

And lastly, the same but focused on driver. Time for a new driver. 5/5

A simple one looking at strokes gained against distance, split by shot type. Pretty remarkable how costly penalties and shots into recovery situations are. Turns out the only thing I'm half-competent at is recovery punch-outs. 4/5

This next one is ugly, but I kinda like it. I spared you by removing all the triple bogies. 3/5

Here's my last 20 rounds (sadly, stretching over 6+ months. ). 2/5

While watching this 0-0 draw, thought I'd try visualizing some personal golf data. The intersection of golfers and sports analysts couldn't fill a foursome, but perhaps a couple of you are interested. First up, a round at Bandon Dunes from earlier in the summer. 1/5

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.

Analytics doesn’t work

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: Paper: Code:

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!.

2019g: Highlight/Lowlight (w/ @Ev_Keane, @phildesaulniers, Mehrsan Javan). We automatically construct highlight and lowlight reels through explainable temporal game models, incorporating levers for producers to fine-tune for multiple purposes.

2019f: Chuckers (w/ @nsandholtz, @jwmortensen). We introduce the idea of spatial allocative efficiency in basketball lineups using spatial hierarchical rank models, allowing us to understand how lineups lose efficiency through who shoots where.

2019e: Fast Not Loose (w/ @yuorme, @Chris_LogiQ, Mehrsan Javan). We take a comprehensive look at pace in hockey from a spatio-temporal perspective, and subsequently study how it correlates with other events of interest.

2019d: Markov-Poisson (w/ @jwmortensen). We develop a flexible nonparametric model for player movement based on conditional Poisson processes, allowing us to capture complex dynamics while incorporating player and team hierarchies.

2019c: Winning Isn't Everything (w/ @SLiQ_Nick94, @yuorme, @Chris_LogiQ, Mehrsan Javan). We look beyond just face-off win% in hockey to understand all the mechanisms by which face-off takers add value (clean wins, directionality, handedness, etc.).

2019b: ACWR-Injury (w/ @OSPpatrick, @DarcyNorman). Using data in soccer and football, we illustrate that training schedule confounds the relationship between ACWR and injury, calling into question much of the previous literature on the subject.

2019a: Soccer EPV (w/ @JaviOnData, @dcervone0). We can measure at every instance the expected value of the current possession by embedding deep learning (to capture 22-player spatio-temporal dynamics) within Markov models (for explainability).

I'm going to tack the papers from this year (Soccer EPV, ACWR-Injury, Winning Isn't Everything, Markov-Poisson, Fast Not Loose, Chuckers, Highlight/Lowlight) on to this thread. Again, credit belongs to my various colleagues. Lastly, adding @Sportlogiq to the list of supporters.