2 years since Expected Threat (xT) was first published! Thought I’d highlight some of the exciting follow-up work that various people have done since:
https://twitter.com/karun1710/status/1096425209765208064
First, a quick statistic that I had a hard time believing – 26,000+ unique people have visited the post and logged a total of 120.8 days (i.e., 4 months) of reading time! 
[Explainers]
If reading a blog post isn’t your cup of tea, this recent podcast by @FanalyticsPod (@ryanbailey57 and @MarkCarey93) is an excellent introduction to xT:
If reading a blog post isn’t your cup of tea, this recent podcast by @FanalyticsPod (@ryanbailey57 and @MarkCarey93) is an excellent introduction to xT:
https://twitter.com/FanalyticsPod/status/1334475562375671809
[Explainers]
If you prefer watching a talk, here’s one from the StatsBomb conference (Oct ‘19) that describes the intuition behind xT and its various applications:
(slides: tiny.cc/xt-statsbomb)
If you prefer watching a talk, here’s one from the StatsBomb conference (Oct ‘19) that describes the intuition behind xT and its various applications:
(slides: tiny.cc/xt-statsbomb)
[Extensions & Applications]
Continuing with the StatsBomb conference, @robwhickman explains how he extended xT to take defensive risk into account:
Continuing with the StatsBomb conference, @robwhickman explains how he extended xT to take defensive risk into account:
[Extensions & Applications]
@rjtavares / @lastrowview describes a method of evaluating off-the-ball player movements by combining xT and tracking data:
medium.com/football-crunc…
@rjtavares / @lastrowview describes a method of evaluating off-the-ball player movements by combining xT and tracking data:
medium.com/football-crunc…
[Extensions & Applications]
@dlareg49 proposed Joint Expected Threat (JxT) as a way of quantifying player chemistry, and incorporates this metric into various visualizations:
@dlareg49 proposed Joint Expected Threat (JxT) as a way of quantifying player chemistry, and incorporates this metric into various visualizations:
https://twitter.com/dlareg49/status/1334107191461040130
[Extensions & Applications]
@dperdomomeza1, @ddgirela, @EveryTeam_Mark, and James Goldring (Twenty3 Sport) used the xT framework, among many other techniques, to study how one can attack set defences:
@dperdomomeza1, @ddgirela, @EveryTeam_Mark, and James Goldring (Twenty3 Sport) used the xT framework, among many other techniques, to study how one can attack set defences:
[Extensions & Applications]
In this piece for ASA, @arjun_balaraman uses xT to identify MLS strikers that excel at creating shooting opportunities for themselves (in contrast to pure goal poachers):
In this piece for ASA, @arjun_balaraman uses xT to identify MLS strikers that excel at creating shooting opportunities for themselves (in contrast to pure goal poachers):
https://twitter.com/arjun_balaraman/status/1156666183346769927
[Extensions & Applications]
Since xT doesn’t make any sport-specific assumptions, it can be adapted to other settings fairly easily. One example is @SamForstner and @yuorme's work on xT for ice hockey:
Since xT doesn’t make any sport-specific assumptions, it can be adapted to other settings fairly easily. One example is @SamForstner and @yuorme's work on xT for ice hockey:
[Extensions & Applications]
@rwohan integrated xT very effectively into a couple of his data-driven pieces to explain Liverpool’s anomalous midfield and Arteta’s cryptic comments:
espn.com/soccer/english…
nograssintheclouds.substack.com/p/what-the-hec…
@rwohan integrated xT very effectively into a couple of his data-driven pieces to explain Liverpool’s anomalous midfield and Arteta’s cryptic comments:
espn.com/soccer/english…
nograssintheclouds.substack.com/p/what-the-hec…
[Visualizations]
xT lends an additional level of granularity to xG-based visualizations. For example, we can now render game momentum timelines in addition to xG timelines, and danger heatmaps in addition to shot maps:
xT lends an additional level of granularity to xG-based visualizations. For example, we can now render game momentum timelines in addition to xG timelines, and danger heatmaps in addition to shot maps:
https://twitter.com/karun1710/status/1167900525775339520
[Visualizations]
@markrstats is one to follow if you’re interested in more xT-based visualizations; a couple of recent examples:
@markrstats is one to follow if you’re interested in more xT-based visualizations; a couple of recent examples:
https://twitter.com/markrstats/status/1359945784431570944
https://twitter.com/markrstats/status/1358104418055512066
[Comparisons]
@MaaikeVanRoy, @p_robberechts, @TomDecroos, and @jessejdavis1 comprehensively compared xT and VAEP along several dimensions and highlighted the fundamental tradeoffs each framework makes:
dtai.cs.kuleuven.be/sports/blog/va…
@MaaikeVanRoy, @p_robberechts, @TomDecroos, and @jessejdavis1 comprehensively compared xT and VAEP along several dimensions and highlighted the fundamental tradeoffs each framework makes:
dtai.cs.kuleuven.be/sports/blog/va…
[Comparisons]
Their group also occasionally publishes data-driven season reviews that contrast the players and actions highly valued by VAEP and xT:
Their group also occasionally publishes data-driven season reviews that contrast the players and actions highly valued by VAEP and xT:
https://twitter.com/jessejdavis1/status/1354769241904439297
[Comparisons]
How does xT stack up against xG as a predictive signal? @MilanKlaasman sheds more light on this topic here:
How does xT stack up against xG as a predictive signal? @MilanKlaasman sheds more light on this topic here:
https://twitter.com/MilanKlaasman/status/1251178569474232328
- Finally, if you’d like to use xT values off-the-shelf, this is a good place to start:
- If you’d like to build your own xT model or extend it, feel free to reach out and I can provide pointers!
If I missed something, please let me know!
https://twitter.com/karun1710/status/1156196523765633024
- If you’d like to build your own xT model or extend it, feel free to reach out and I can provide pointers!
If I missed something, please let me know!
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