Well played...
Need to develop an analytics department
- Thread starter UltimateFrog
- Start date
Well played...
The Artist Formerly Known as PhormerPhrog
Full Member
Was telling a buddy this very thing yesterday. Have also been pushing my company to do so with resistance. It will be a slow and painful death if it doesn't happen.
The Artist Formerly Known as PhormerPhrog
Full Member
I love her.
Eight
Member
Gehörnter Frosch
Tier 1
I've said the same thing for years.
The EV of that long filed goal attempt (that we made) vs. the EV of going for it on 4th and eventually scoring a TD has to weigh toward the latter.
Without doing the analysis, I bet the EV of kicking that FG was probably about 1 point, and the EV of going for it was probably 2.5 points. That's a huge difference, and we basically have our opponent 1.5 points my kicking.
Our coach doesn't realize that these missed ponts add up over time. He's like a gambler who plays on "gut feelings" and "soul reads" rather than Game Theory Optimal strategies.
The best coaches and leaders use a combination of both...but most use analytics to inform their decisions...and weigh risk vs. reward.
Not trying to score at the end of regulation, with time outs remaining, is another example of this incompetence. The EV of trying to score was probably 1.5 points, and the -EV of failing and getting scored on was to lose was probably .25 points. There was literally no logical reason to not try and win the game right there.
Nothing but emotion and superstitions run our program.
The EV of that long filed goal attempt (that we made) vs. the EV of going for it on 4th and eventually scoring a TD has to weigh toward the latter.
Without doing the analysis, I bet the EV of kicking that FG was probably about 1 point, and the EV of going for it was probably 2.5 points. That's a huge difference, and we basically have our opponent 1.5 points my kicking.
Our coach doesn't realize that these missed ponts add up over time. He's like a gambler who plays on "gut feelings" and "soul reads" rather than Game Theory Optimal strategies.
The best coaches and leaders use a combination of both...but most use analytics to inform their decisions...and weigh risk vs. reward.
Not trying to score at the end of regulation, with time outs remaining, is another example of this incompetence. The EV of trying to score was probably 1.5 points, and the -EV of failing and getting scored on was to lose was probably .25 points. There was literally no logical reason to not try and win the game right there.
Nothing but emotion and superstitions run our program.
Gehörnter Frosch
Tier 1
Sorry for typos. I'm on mobile. Corrections made.
AroundWorldFrog
Full Member
Good idea. Can they catch the damn ball?
The Artist Formerly Known as PhormerPhrog
Full Member
you love her or you love her's?
Those. Are. Exceptional.
Uncle_Frog
Active Member
Confirmed boob guy. Nice.
The Artist Formerly Known as PhormerPhrog
Full Member
Pretty much a you fine and you were born a woman and still have all your parts guy.
It is really hard to tell nowadays on who was born a woman with all this transgenderism going around...
PurpleBlood87
Active Member
Did you know TCU has the top-ranked defense in the Big 12?
Moose Stuff
Active Member
Eight
Member
who said football is?
patriots have been using for years, same with some other nfl teams. big difference is they don't talk about it much outside their walls.
there are number of college teams and if you think about it much of what the hunh's are based upon go against conventional football wisdom
I don't know, but compared to most other sports, there are way, way more variables involved in football so I don't know how much analytics can help. I know coaches should go for it on 4th down more, that takes about 3rd grade math to figure out those probabilities. But how do you account for field conditions, wind and temperature, injured players, inexperienced QBs, personnel mismatches at various positions, emotion/momentum, the list goes on and on.
jake102
Active Member
With how many coaches (NFL and college) botch the clock, 4th down, punting, etc etc just fixing those things would be a start. And TCU routinely does all those things wrong.
flyfishingfrog
Active Member
I am involved with a company that provides data and provides "analytics as a service" for a group of about 12 professional sports teams in a couple of different leagues.
I can tell you that one of the largest barriers to entry for college teams is the lack of actual data collection going on across the NCAA. Yes, they collect what the results of the play (who had the ball, how many yds, downs, etc) and teams do a lot of data collection on their own for their own games around what formations were run, personnel on the field, who did what they were supposed to and who didn't.
But most of that data collected by the individual teams is manually collected, has varying levels of quality, not consistent in measurement, etc.
Beyond that, the data that is available is basically the type of content you see on a game simulcast type application - which is beneficial but not going to "change the game" for most programs. The analytics are literally going to be basic stuff like you have a 20% of scoring a TD and 35% chance of a FG when going for it on 4 and 2 inside the other teams 40 thus that has an effective result of 2.7 pts vs the 65% of the time you fail results in the other team getting an effective result of 2.1 pts so it nets to a +.6 decision.
While the pro leagues where the NFL, MLB, NBA and NHL work with each franchise to develop the standard data model, processes and technology to collect data in a high quality manner so that teams can be bench marked against each other. They also collect a level of detail just not currently available to college programs. Few college teams would spend the money to put "sensors" in the pads of their players, use visual analytics to calculate acceleration and pattern consistency, cut rates, ball spin rates, angle of attack, launch speed, etc and they are definitely not going to get that data from their opponents for example. While the pro leagues collect and distributes that type of information across the league for a fee from spring training through the end of the playoffs.
And a lot of that data is available real-time during the game to support half time, between inning type adjustments (or in the case of the Astros - real time during the at bat).
Thus the pro leagues are light years ahead - largely because of the data they have access to not because the actual data science is any more advanced.
I can tell you that one of the largest barriers to entry for college teams is the lack of actual data collection going on across the NCAA. Yes, they collect what the results of the play (who had the ball, how many yds, downs, etc) and teams do a lot of data collection on their own for their own games around what formations were run, personnel on the field, who did what they were supposed to and who didn't.
But most of that data collected by the individual teams is manually collected, has varying levels of quality, not consistent in measurement, etc.
Beyond that, the data that is available is basically the type of content you see on a game simulcast type application - which is beneficial but not going to "change the game" for most programs. The analytics are literally going to be basic stuff like you have a 20% of scoring a TD and 35% chance of a FG when going for it on 4 and 2 inside the other teams 40 thus that has an effective result of 2.7 pts vs the 65% of the time you fail results in the other team getting an effective result of 2.1 pts so it nets to a +.6 decision.
While the pro leagues where the NFL, MLB, NBA and NHL work with each franchise to develop the standard data model, processes and technology to collect data in a high quality manner so that teams can be bench marked against each other. They also collect a level of detail just not currently available to college programs. Few college teams would spend the money to put "sensors" in the pads of their players, use visual analytics to calculate acceleration and pattern consistency, cut rates, ball spin rates, angle of attack, launch speed, etc and they are definitely not going to get that data from their opponents for example. While the pro leagues collect and distributes that type of information across the league for a fee from spring training through the end of the playoffs.
And a lot of that data is available real-time during the game to support half time, between inning type adjustments (or in the case of the Astros - real time during the at bat).
Thus the pro leagues are light years ahead - largely because of the data they have access to not because the actual data science is any more advanced.
nwlafrog
Active Member
A truly phenomenal set of feeders
