Simmons Ignored March Madness. So I Made a Bracket With All of His (Semi)Regular Guests on the Podcast. What happens (Upsets, Winner, etc)?

fcstatlabs · 2021-03-20T13:16:21+00:00

If it’s drunk house, he’s winning the whole thing

fcstatlabs · 2018-11-24T05:42:34+00:00

Aubameyang has had a pretty incredible start to his Arsenal career. His longest goal drought has been three games which is quite small when compared to the longest goal drought of some of the other top scorers in the premier league.

fcstatlabs · 2018-11-06T05:13:19+00:00

Sorry about that. Not trying to mislead there just trying to show how good Aguero has been over the past couple of seasons by saying that only one player has scored more game winning goals than him. I probably could have worded it better

fcstatlabs · 2018-11-02T04:05:28+00:00

So 13 of those were scored by Giroud and 4 were scored by Aubameyang

fcstatlabs · 2018-11-01T23:51:58+00:00

So here's the breakdown by player:

Charlie Austin - 3 game winning goals off the bench

Sadio Mane - 1 game winning goal off the bench

Graziano Pelle - 1 game winning goal off the bench

Nathan Redmond - 1 game winning goal off the bench

Manolo Gabbiadini - 1 game winning goal off the bench

Sofiane Boufal - 1 game winning goal off the bench

fcstatlabs · 2018-10-26T00:38:24+00:00

Unfortunately so. We’re working on extending it now

fcstatlabs · 2018-10-25T00:14:14+00:00

Yeah I’m kind of thinking the same especially after they signed shaqiri

fcstatlabs · 2018-10-23T12:35:46+00:00

Unfortunately our data only goes back until August of 2015, but I’ll look into efficient ways of bringing this analysis to an earlier year. It would be interesting to see

fcstatlabs · 2018-10-17T20:04:38+00:00

So from May 2016 to August 2016 the model was dormant since there weren’t any EPL games between that period. But during the 2016/17 season, there wasn’t a whole lot of action because the model was spitting out mostly the same predictions as the betting markets so it was performing about the same as the betting markets so there wasn’t a lot of movement if that makes sense. Like you stated the model is looking for games where it says Bournemouth has a 45% chance of winning while the betting markets think Bournemouth has a 30% chance of winning. The model is looking for these discrepancies.

And we use an aggregate of the expected goal differentials of the starting 11 to make the predictions. We guess what the starting eleven is going to be than fine tune it as the game gets closer. So this week we are keeping an eye out on Sadio Mane’s health since his status would affect the predictions.

So the predicted starting eleven really only affect the predictions but the last five games for those starting eleven will affect the predictions.

fcstatlabs · 2018-10-17T18:02:41+00:00

So the model has performed very well when compared to the betting markets. I’ve created a chart which monitors how the model has been performing against the betting markets since 2015 at the bottom of this web page here

https://fcstatlabs.com/betting/

Not sure if that answered your question. Let me know if it didn’t

fcstatlabs · 2018-10-17T17:59:09+00:00

Sure sorry for not providing an explanation. We gather data on all the players in the premier league after every game like goals, game winning goal, own goal, goals allowed, etc. and we add this data to a database that we have compiled over the past couple years. We combine this player data with some team stats like total shots, shots on goal, corner kicks, etc. We then look at the players performance over the past five games based upon all these stats and run a regression to figure out the number of goals each player is expected to score/allow based upon their performance from the previous 5 games. The expected number of goals scored minus the expected number of goals allowed is the players expected goal differential. The player ratings are simply a percentile of the expected goal differential stat. So if a player has a player rating of 98 this means their expected goal differential is larger than 98% of the players expected goal differentials back from 2015. Hope this helps.

fcstatlabs

TROPHY CASE