Brilliant, Scott. I love the expression that 'no model's perfect, but some are informative', and I definitely think yours provides a lot of important info. Good explanation at the end re added complexity perhaps not being worth the time/effort.
The thing that concerns me most with the models on Arsenal's increasingly high odds of winning the league (ie, 50% on fivethirtyeight) is the possibility that things just fall apart after an injury to any of a few key players...I don't think City suffers from that downside as much, though KDB or Haaland going down wouldn't help. Saka/Martinelli/Odegaard/#5 go down, and I think our odds drop by 5-10 percentage points. I still think something similar with Jesus that hasn't yet been reflected in the models. (I have no empirical evidence, just my feeling which I'd love to prove/disprove)
Anyway, I think this falls in the category of the 'added complexity isn't worth the marginal gains', but the fact that City is the only club with two top tier 11s whereas we can only pray to the injury gods is an important differentiator that protects City's downside. Maybe there's an easy way to capture 'percent of team salary that is expected to be out with injury in the next few games'? That would reflect the injuries that already happened and expected downside to result. There would also be the more complex measure that shows the difference in value between the starter and backup (and likelihood of injury to starter), which would reflect downside. Again, all this is likely not worth the effort, but I still have ptsd after last season, so yeah.
Other factors that would be easier to implement and that I think matter relate to how many days of rest teams have - I think 3 vs 4 days is an important distinction that I don't think models capture. Again, maybe I'm just thinking of anecdotal evidence, but coaches often complain about the lack of rest between games being a disadvantage, so I'm guessing it's a factor.
Thanks again for your work on this. Some very cool stuff!
This is a fabulous walk through of your process!
Brilliant, Scott. I love the expression that 'no model's perfect, but some are informative', and I definitely think yours provides a lot of important info. Good explanation at the end re added complexity perhaps not being worth the time/effort.
The thing that concerns me most with the models on Arsenal's increasingly high odds of winning the league (ie, 50% on fivethirtyeight) is the possibility that things just fall apart after an injury to any of a few key players...I don't think City suffers from that downside as much, though KDB or Haaland going down wouldn't help. Saka/Martinelli/Odegaard/#5 go down, and I think our odds drop by 5-10 percentage points. I still think something similar with Jesus that hasn't yet been reflected in the models. (I have no empirical evidence, just my feeling which I'd love to prove/disprove)
Anyway, I think this falls in the category of the 'added complexity isn't worth the marginal gains', but the fact that City is the only club with two top tier 11s whereas we can only pray to the injury gods is an important differentiator that protects City's downside. Maybe there's an easy way to capture 'percent of team salary that is expected to be out with injury in the next few games'? That would reflect the injuries that already happened and expected downside to result. There would also be the more complex measure that shows the difference in value between the starter and backup (and likelihood of injury to starter), which would reflect downside. Again, all this is likely not worth the effort, but I still have ptsd after last season, so yeah.
Other factors that would be easier to implement and that I think matter relate to how many days of rest teams have - I think 3 vs 4 days is an important distinction that I don't think models capture. Again, maybe I'm just thinking of anecdotal evidence, but coaches often complain about the lack of rest between games being a disadvantage, so I'm guessing it's a factor.
Thanks again for your work on this. Some very cool stuff!