u/EntertainmentCalm889

https://preview.redd.it/yb2u49fq48ih1.png?width=1513&format=png&auto=webp&s=dc361e2e3f85d21b5fd760c74efaa608c668f012

It's been more than a month since my last update of my LoL Esports model. Just wanted to give a short update. Apart from free predictions and data I have introduced new markets for Total Kills (so far they look good, it's been about 2 weeks since I'm tracking them) and an API for developers to access with free tokens if anyone is interested.

Interestingly enough the last 7 days have been bad for series predictions which are an overall positive market, but good for pretty much everything else with the PCA model being the best in both the last 7 days and the last 3 months period. LCP has stood out as a very lucrative league with 16/17 +EV picks being correct.

What do you guys think about LoL and Esports betting in general and what markets do you prefer the most?

https://preview.redd.it/2phcsbmetsah1.png?width=1714&format=png&auto=webp&s=1a8643bc607e160519b2248052edc048e86b3391

Hey everyone.

an API has been added to riftcast for anyone who wants to try it out. There are free monthly credits that should be enough for setting it up + getting a few predictions in or fetching team's historic data.

For anyone not interested in development/APIs the site has free predictions for guests/free users you can try out.

Let me know your thoughts and if you'd like to see something changed

https://riftcast.gg/

Now with about a month of tracking ROI, here's how my 4 models compare on the most profittable market I've had so far - Series Winner.

https://preview.redd.it/nch22fckgj5h1.png?width=1447&format=png&auto=webp&s=1322267543e18f6a564d369bd8acf04ad0afbb0b

The series tracked are less than 50, because only positive EV markets get tracked. Anything with a bad market odd not worth the risk gets ignored.

The record of each model is different, because only positive EV predictions go into their record. If a model predicts a team to win with a small amount of certainty, but the Market Odd (let's say from bet365) is 1.20 then this is not a positive EV record for this model so it gets ignored (no matter if the prediction is correct or not).

After all in the world of prediction betting we only succeed if we target positive EV markets :).

Also I've recently added the Series Handicap, Total Maps and Exact Score markets too and I'll report the progress here once we get around a month worth of data on those markets too.

Site name is https://riftcast.gg/

Posted here a while back about the LoL Esports prediction site I've been building

Wanted to share an update because I've added something that wasn't there before: public ROI tracking against actual bookmaker odds.

I have the following models:

FastTree - free tier, ML gradient-boosted trees,

LightGBM - A more meta-aware model with better patch tracking,

PCA Sweep - multiple classifiers on PCA-reduced features,

and a Consensus weighted average of the three.

Two variants of each: Series (pre-game) and Post-Draft Games. So 8 model outputs total.

All models are trained on S15 and S16 data. Each day they get retrained by using the newly completed games of the day.

Here's the Consensus Model Overall ROI: 15% for Games and 13% for Series

https://preview.redd.it/6g1uj9c5rj3h1.png?width=1035&format=png&auto=webp&s=163e479e736a80f2a70414e8b7f98386ba932258

A game is counted into the positive EV Series/Games that go into this graph when the model favored team in the Series/Games has better than expected Odds by the bookie, meaning that according to the Model the risk-reward is worth it.

The site is https://riftcast.gg/

Has anyone else done a similar ROI mechanic using actual betting odds for LoL Esports?

Hey everyone. I built https://riftcast.gg/, an ML prediction system for LoL Esports with both training stats visible and historic data tracked (if model predictions were correct or not).

The setup:

- 3,091 pro matches in the dataset across 272 teams and 43 tournaments (so far), covering all major regions (LCK, LPL, LEC, LCS) and minor regions

- Series-level predictions (pre-match) and game-level predictions (post-draft)

- Three models running in parallel:

- FastTree (free tier baseline, simplest features)

- LightGBM with patch/meta-aware features (tracks game duration trends, team performance gaps between recent patches and all-time, format interactions like is_bo5 * elo_diff, etc.)

- PCA Sweep — runs a 7000-config hyperparameter search for ~5 hours weekly, PCA-compresses the noisy draft features

- Plus a Consensus prediction combining all three

**Feature engineering:**

The series model uses ~80 features after filtering. Heavy use of:

- Differential features (Blue stat - Red stat) to avoid teaching the model side bias

- Decayed all-time stats + Diff5 rolling windows for recent form

- A custom Elo system with cross-league calibration (this is what handles international events, which only have ~20 games of historical data)

- Hand-crafted composite features (Diff_Composite_EarlyGame, _Combat, _Vision, etc.) to compress correlated signals.

The draft model adds champion-level features: per-lane Overall/Counter/ Mastery/Meta scores weighted by Samples confidence, synergy by lane-pair (Top-Jgl, Mid-Jgl, Bot-Sup), and per-lane "LaneEdge" composites.

I have an "Uncertain tag" which excludes prediction results for predictions with less than 55% certainty which is also shown in the UI for transparency

Accuracy across the last 2 weekly reports published (75 series, 209 games):

https://preview.redd.it/6c3hogqx6a0h1.png?width=1539&format=png&auto=webp&s=ae7009a43f6e84f41b732dbe2d79a75ceb029da3

I also track each model's performance per league and show it on each upcoming match prediction. For example, Consensus (the aggregate from all models) is yet to make a wrong Series prediction for LCS (17/17 correct) and has a fairly good accuracy for Game (Draft) Predictions as well (28/35 correct)

https://preview.redd.it/wv57dtz08a0h1.png?width=930&format=png&auto=webp&s=e0beebbce44f895459e73fd5c9b2d21ae80fabd3

Where I think it's weak:

- International events (~20 cross-region games in dataset) — Elo helps but cross-region calibration is shaky

- LightGBM volatility week-over-week (76%/70% vs 69%/80%) — patch-aware features may be over-correcting

Any feedback will be much appreciated, thanks!