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There's nothing jumping out at me as original thinking in this article or the paper abstract that points to the paper being worth more study. All of what has b
by PaulRobinson 5y ago
There's nothing jumping out at me as original thinking in this article or the paper abstract that points to the paper being worth more study.
All of what has been mentioned has been conceived of, thought about and discussed before, and some years ago. I've seen more advanced thinking in some models used to make bets on football matches.
As a Man City fan, I'll try and make this sound less snarky than it will come across, but it's going to be snarky: it's good to see Liverpool are finally - with Deep Mind's help - catching up to where many other teams in the league were more than half a decade ago.
Most players and coaches are not particularly interested in these models and the most useful applications seem to be related to scouting and the transfer market - "I need a replacement for player X, but 10 years younger and with a better left foot", is the sort of problem that data science is already helping with - and the clubs themselves prefer to put their money onto the field of play rather than expensive and experimental ML training, so the market for this sort of work is limited.
The challenges in this field are not really about data, algorithms or computational capability of the hardware.
The main issues are of applicability, cultural fit and economics.
Finding methods for addressing _those_ issues might be more useful to mainstream AI adoption than the data, algorithm and hardware problems many seem to look at first.
- wdb 5y agoSports analytics is an interesting field, worked in it, you can use it for keep tracking of the health of your players. To know when you need to keep them from playing the next game or stop them from continuing their current game.
- PaulRobinson 5y agoOn health tracking stuff, rugby was some years ahead of any other sport - is that still the case?
- thom 5y agoLiverpool have been doing this for years without Deepmind’s help (it’s been more than five years since Michael Edwards was decried as a laptop guru who betrayed Brendan Rogers, for example). The analytics team at City Football Group has only really been built over the last year (but are very good). I don’t know that any of their dabbling before that was at this level. Your point about applicability is totally right though, see Arsenal with StatDNA and the arguably weak impact of Barcelona’s Innovation Hub. I’d argue that Leicester’s use of (at the time) very rudimentary stats, nevertheless well integrated into their process, was a big part of their sudden success.
- PaulRobinson 5y agoI knew - and still know - the data guys from CFG who were there in 2016-ish. It's no doubt undergone a lot of changes, but it's definitely not really been built over the last year.
- thom 5y agoI know Brian Prestidge has been there a while and obviously has stats pedigree going back before even most of the data providers. But I don’t believe he’s been doing most of what Deepmind are talking about here (or which Will Spearman has been working on at Liverpool), which (as I understood it) is why they’re hired people like Laurie Shaw etc over the last year. Happy to be corrected, they all seem like great guys.
- hogFeast 5y agoThe Andy Carrol purchase? Liverpool's process clearly developed over time.
- theja 5y agoAgree with you that figuring out the cultural fit and applicability is super important. I learned a lot firsthand about this while trying to set up a decision support tool (with a supervised learning algorithm underneath) for a few teams in NASCAR back in 2012 [0]. [0]: https://www.liebertpub.com/doi/pdf/10.1089/big.2014.0018 https://www.liebertpub.com/doi/pdf/10.1089/big.2014.0018