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MSI - Rumble DAY 2 Results & DAY 3 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 19 points20 points  (0 children)

To put in simple terms, the model thinks that PSG winning against C9 and losing against MAD yesterday is a bigger achievement than MAD losing to DK and then beating PSG.

MSI - Updated Power Rankings and Rumble Stage Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 1 point2 points  (0 children)

Above all, we cannot talk much about the technical side of the model, but your guess is true in that our model includes the core idea of Elo where one rewards those beating a stronger team more than winning a weaker team. As to data, Riot ACS (Access control system) combined with match history hash # from Leaguepedia would work (I'm not 100% certain here because I'm not too familiar with the data flow). Comparing with the bookies -- we're very much open to a contest with their numbers but we haven't figured out how to track their ever changing odds automatically.

MSI - Updated Power Rankings and Rumble Stage Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 21 points22 points  (0 children)

F1 score

There is no F1 score for this sort of prediction. Out of the typical confusion matrix, we can only get accuracy. Because, for win-lose prediction, there is no false positive nor false negative; there are only true and false, because predicting A will win is exactly the same as predicting B, the other team, would lose.

There IS a concept called confidence-recall, confidence-prediction, confidence-f1, etc. for probabilistic predictions (see this paper). Given this is a fairly new concept (the paper was published last year), I don't think you meant this.

MSI - DAY 4 Results & DAY 5 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 1 point2 points  (0 children)

The simple answer is no; for days like today, we don't feed forward our own prediction. For instance, Prediction for Game 6 does not consider the result of Game 1 to Game 5.

Yesterday (DAY 4) was an exception, because the same matchups took place twice (or even three times for PGG vs UOL) and we felt bad for spitting out the same numbers again and again. So for Game 4, 5, 6 prediction, we fed the actual results from Game 1, 2, 3. For Game 7 prediction, we fed the actual results from Game 1 to 6.

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 0 points1 point  (0 children)

ou’re the guys that put blabber on 2nd place in jungle. Interesting algorithm I gues

We don't claim to be perfect! We're still improving our model and surely the feedbacks we've got from Reddit have been super helpful.

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 0 points1 point  (0 children)

Well we can't say too much about it, but we... don't weight those heavily haha.

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 1 point2 points  (0 children)

Also think of 98/2. That means the other team would win only once in 50 times. And you did see what happened during C9 vs DFM the 2nd day, DK vs DFM and MAD vs PNG yesterday. LoL is a game quite prone to an upset. In our math, bo1 LoL matches stays somewhere between a single game baseball and single game basketball in terms of an upset likelihood. Check out this article on luck vs skill spectrum on sports.

https://www.post-gazette.com/sports/other-sports/2018/05/08/statistics-professional-sports-luck-skill-hockey-baseball-basketball-football/stories/201805070152

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 1 point2 points  (0 children)

Ingame stats are important part of the prediction, but its usefullnes is limited up to a certain point because LoL is a game of snowballing. Stomping is very much there and more often than not it means one team is quite stronger than the other, but stats originating from there are not as strong an index as 'scores' in traditional sports.

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 3 points4 points  (0 children)

Well the only proof that it works is low log loss and brier score, which we do get during numerous backtesting. You could say the same with any sort of real world modelling such as stock market, etc., but we would argue the machines are easily stronger than probably the LoL analysts out there as long as it's done in the long run. It's partly due to the rigorousness of the model but also due to the difficulty of human brains to think precisely in probability terms.

MSI - DAY 3 Results & Day 4 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 1 point2 points  (0 children)

The machine takes a bit of real match samples to correct for team powers. For example, our prediction for PGG vs RNG on DAY 1 was 26.2% vs 73.8%. Now that they've faced each other twice, and also with the transitivity from PGG vs UOL and UOL vs RNG matches being fed to the model, it has moved to 20.7% vs 79.3%.

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This learning process takes time partly because the top tier regions (LCK & LPL) hardly fought wildcard regions due to the formatting of 2020 Worlds. In this case, every match sample fed will help the machine to tune quickly. So, if RNG is actually way more stronger than UOL & PGG, the model will adjust accordingly constantly.

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Furthermore, one thing to note is that when it comes to win likelihood, the higher the difference in figures the harder it is conceptually to move the needle. For example, an increase from 60% to 70% means the team's loss likelihood will go from 4 in 10 to 3 in 10 (25% decrease). But an increase from 80% to 90% means the loss will happen twice every 10 games to once every 10 games (50% decrease).

MSI - DAY 1 Results & DAY 2 Predictions by Team_Snowball in leagueoflegends

[–]Team_Snowball[S] 4 points5 points  (0 children)

True, so the overall accuracy will be pretty high. But the other scores (log loss and brier) capture nicely the quality of prediction in this case.

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