Classification modeling using ONLY home team data

Every classification model example I've seen uses the binary target on the home team winning or losing. So that's how I've always built my classification models. Target is "home_win" or some variation of that.

I started thinking today about cutting my feature set in half and only using data related to the home team. Partly as an efficiency exercise. Reduce the number of variables in play, don't throw the kitchen sink at the model.

What prompted this was my collection of "differential" variables. For example, if I have "away average points" and "home average points," I'll turn that into "home average points differential" and use that engineered feature instead of the two individual features. But my differential features are always keyed to the home team, and those features are consistently among the best performers.

I'll backtest my theory of course, but I was curious how many of y'all use models that emphasize home team data vs a mix of both home and away.

in algobettingby Bits_Bytes_Bucks

50%

13d

FantasticAnus

13d

Representing both the home and away team data is essential to have any chance of a good model. The features for the home team are identical to the away team in my models, I let the model itself determine how to weight all those factors. With appropriate regularisation and an adequately large dataset I don't find that this poses any great issues.

GarbageTimePro

13d

have you ran your model against WNBA props this season?

FantasticAnus

13d

No, I only do NBA stuff, and not props in general.

Governmentmoney

12d

Mate, this is peak nonsense

nfadd34

12d

Yes differential features can be very informative. I’ve done exactly that with my MLB model and it’s produced incredible results so far. Subtracting away stats from the home stats still uses both teams so yes you’re cutting the length of features in half but not necessarily the data if that makes sense

DotSlashSports

10d

Youre getting shit on here because you explained it poorly.

You can definitally use differentials instead of home and away stats for certain things. For example:

model1_features = [home_pts, away_pts, feature_n]

will have a similar confusion matrix as

model2_features = [point_differential, feature_n]

what i would do it initially throw the kitchen sink at it like this:

model3_features = [home_pts, away_pts, pts_diff, feature_n]

and then just do some principle component stuff, etc to kill some features

Bits_Bytes_Bucks

10d

Ha. I don't feel like I'm getting "shit on," it's just the usual % of users here who don't know how to contribute to or advance a discussion, so they default to shutting down any further conversation. Welcome to the Internet. I appreciate you advancing the conversation.

It makes sense to me that differentials basically represent the exact same thing as away vs home stats, as you've explained it. Using [home_pts, away_pts] is likely going to get similar results to [pts_diff] -- totally agree.

My models (all sports) typically use mirrored features for both home and away. So, for MLB, [a_woba, h_woba, a_bullpen_era, h_bullpen_era] and so on. Then I'll created "_diff" features for all of those, and go full kitchen sink, at least to start.

But yeah, I actually was saying (or wondering aloud), in the original post, if you could potentially get better/improved/more efficient results by just using home team features. It seemed plausible, at least. Throw 60-80 home features at it, targeting "home_win", and at some level the model should figure out the relationships and impact to the target. It was really just a curiosity question of how much opponent stats matter. You just as easily flip it: what happens if you take out all the home features and only use away features?

Anyway, end of the day, I like to tinker with different theories and wanted to see if anyone else had gone down this road before and had any empirical evidence one way or the other.

(For the curious: I did try stripping out the away features and cycling through different home feature combos. And I did get slightly better metrics, but not major enough to conclude anything. Differentials alone still seem to get the best results, FWIW.)

DotSlashSports

10d

I actucally did this a bit ago

X = data[['home_elo', 'away_elo', 'elo_delta', 'home_net_rating', 'away_net_rating', 'net_rating_delta']]

Deltas perform better from what iv seen, allowing models to generalize better for win loss classifications. having home_pts away_pts is too noisy. If i was trying to predict total_points from a regression i probably would not include the delta

LawyersGunsMoneyy

Yeah I agree with others, this is very poorly explained.

I'm not 100% sure what you mean. If you mean you're going to look at differentials exclusively rather than values for both home and away, then yes this could be part of a successful model. I use these techniques (in addition to individual team values) in my own models.

If you literally mean "I'm only going to look at data from the home team and ignore the away team", then this is an asinine undertaking. The Bills (home team in this example) chances of winning are drastically different vs the Jaguars as compared to vs the Chiefs.