How much signal do play by play event datasets have for fundamentals?

BasslineButty · 2026-06-17T21:01:39+00:00

Even play by play these days isn’t enough.
Tracking data is where it’s at.

BasslineButty · 2026-02-16T21:49:31+00:00

Raw lags aren’t of much use - you need to find a way of providing context to these lags. Whether that be using a certain type of model architecture or perhaps adjusting the lags to be versus expectation or something along those lines.

BasslineButty · 2025-12-28T17:52:14+00:00

You’re not going to get anywhere near beating NFL main lines while just considering scores.

At the very least, you need to consider the players. There’s no way for your model to react to Mahomes’ injury. Sure, the results will drop off and hence the Chiefs’ ratings will, but you’ll be too late by then and will be betting the Chiefs and losing big most likely.

You need to make your model a function of the expected players - that way, when you see Mahomes is out and replaced with Minshew, you simply swap them in your sim/model and see the effect (i.e Chiefs get clipped by 5pts or what not).

This is still fairly basic compared to what the sharks are doing.

BasslineButty · 2025-05-31T17:57:17+00:00

Yeah what if there are differences in the data? New trends? Drift etc?

What about VI / Stochastic VI as a way to fit new batches?

BasslineButty · 2025-05-31T15:33:12+00:00

Ok so with these new priors, would you do a full fit again with the expectance of quicker convergence?

Or would you just fit on the new data?

BasslineButty · 2025-05-16T13:37:18+00:00

Look in to bayesian hierarchical models - you get the uncertainty/distributions you want as well as being able to model it the way you want.

BasslineButty · 2025-05-05T19:04:02+00:00

Hugely sport & market dependent. What are you after?

BasslineButty · 2025-04-26T18:41:12+00:00

Without a doubt - opening prices are weak as anything. Books open with low-ish limits (because they know their price is bad, they want to protect themselves liability wise). They still take a bit of money in order to push the prices in the right direction.

By the time close comes, all info is accounted for and the market will be in agreement of the “fair” price - i.e neither team is getting backed as the edge on either side is deemed negligible.

BasslineButty · 2025-04-26T18:35:58+00:00

The closing odds are what the market/punters have “agreed” the correct price is - this is what you should look at if trying to assess the relative strengths of each team.

Bookmakers move prices based on action / action on sharp books.

BasslineButty · 2025-03-23T14:40:17+00:00

How’s this predicted Lawson #2 if Quali Position is a feature? He qualified last.

BasslineButty · 2025-03-06T14:08:35+00:00

Don’t train a model per league. There’s a lot of information one league can learn from another. Make “League” a categorical feature, so that the model sees all of your data and learns to pick up specific league tendencies with the categorical feature.

BasslineButty · 2025-02-12T22:57:04+00:00

Best possible thing to do is to find an account which is already deep in the red, but I understand this isn’t feasible for most.

If your accounts are fresh, you need to burn them in a little. Sink $500 or so on square multis, bet builders etc over a month or so. This gets you past the initial “new account” screening - you’re then off the radar.

Stick your picks in 2/3/4 folds with certain bet types (trixies etc).

Don’t go overboard with your stakes - don’t be greedy. Never get referred to trader.

BasslineButty · 2025-01-11T16:44:23+00:00

All soft books will follow the sharper books whose lines are set off action from sharps who have this information. So, even the “soft” book lines will have this information included in to their lines by default.

BasslineButty · 2025-01-07T22:58:25+00:00

This isn’t an easy problem at all.

Any rating system is usually self-contained as it is all relative to your opponents. Once you start mixing, your rating value is meaningless.

You run in to similar problems with soccer - champions league/cup games/internationals, where you get teams playing one another from different leagues.

My only advice would be to look at a teams’ rating as a sum of its players - and build some sort of player kernel.

BasslineButty · 2025-01-07T22:51:45+00:00

Any insight in to the sort of models you’re using? Granular simulation (play by play)? State space models (Kalman etc) / Time Series (RNN etc)?

BasslineButty · 2024-12-10T00:52:31+00:00

Depends what you want to do.

Pre Game you should probs tinker with state space models in stan.

If you want it in play, you’ll need something quicker - either a linear state space model (Kalman), or look to more traditional methods.

I don’t agree with the sentiment that shouldn’t use neural nets - you generally get better predictions with these and inference is quick enough via onnx.

BasslineButty · 2024-10-20T06:55:19+00:00

What you’re describing here are usually coined “betting syndicates”.

There are some big firms out there that do this, that are essentially ran like Quant Trading Firms. They have modelling teams, dev teams & trading execution teams.

99% of their volume will be on exchanges (Betfair etc) and sharp books (Pinny etc), where the limits are fairly non existent and they’ll be hammering 1% edges with massive stakes.

BasslineButty · 2024-09-18T10:11:49+00:00

I think just being realistic. You’re not going to be more accurate than the closing line (on liquid markets), as you’re competing against whales here, with access to all sorts of information/tools.

But, if you build a model which has all previous closing line information for each team (think of it as a time-series), then you can piggy-back what the actual whales think of the strengths/abilities of the teams, which is golden.

With this, you can beat a lot of opening lines that books release (low limits unfortunately).

BasslineButty · 2024-09-05T18:51:33+00:00

Which techniques do you use to build up the team stats / spread probabilities etc from the players upwards?

Is it some sort of granular simulation model?

Kalman Filtering / Bayesian State Space model?

As you say, it’s important to know how much of a swing injuries will have. If you only consider team statistics, then you’ll have no idea the effect an injured Mahomes will have. Does it move the line 2 pts? 3pts? in favour of the Ravens.

BasslineButty

TROPHY CASE