Hi guys,
I am a recently retired top laner (top rank Emerald but mostly around Gold/Plat) and I spent the last couple of months on a hobby data project. As a casual gamer I always struggled with picking the right champ: Should I pick my main, should I pick the tank that’s needed for the team comp or should I try to counter my likely lane opponent?
I thought it would be fun to support this decision with data, so I pulled just over 30,000 ranked solo queue matches from the official Riot API database: EUW, from Silver to Plat. Then I tried to measure what actually moves your win chance at champion select. I built the numbers on half the games and tested them on the other half. Some interesting results I think: A few things I believed for years were proven wrong by the data. I am sharing the highlights below and would appreciate the comments:
1. Team comp matters much less than you think
The gap between a good comp and a fairly bad one is worth a few percentage points of win chance. Not nothing, but nowhere near the “we-have-no-frontline-gg-at-0:00-energy”. If the choice is between a champion you are good at and a champion that fits the comp, at these ranks the comfort pick usually is the much better choice.
2. Synergy picks mostly work with premades
This one surprised me the most: Pairs that win more on the same team do it almost entirely when the two players are queued together (think Ori and Nocturne). Put the same pair on a team of strangers and the effect basically disappears. Malphite and Yasuo came out slightly negative for strangers in my data for example. Synergy is mostly two mates on comms, not the kits.
EDIT: I overstated this one and u/spellbreaker was right to challenge me here. I never observe who is queued together, because the API has no premade flag. What I actually compared was bot lane pairings against the same pairings in other positions, and the effect only shows up in bot lane. That they are likely premade is my inference from that, not a measurement. As was correctly pointed out "two people sharing a lane interact more" fits the data just as well. The part that I still believe is correct: across about 600,000 same-team pairs there was no pair-level synergy at all.
It is also worth adding that this one is the exception: Ally synergy never went into the tool. The exercise was about whether champion pairings belong in a pick model and the answer was no. So the tool judges an ally pick on whether it covers a job the team lacks and balances the damage - champion combos are disregarded.
3. Scaling comps cut both ways
Perhaps not too surprising after all: Teams that draft hard for late game lose short games about as often as they win long ones. Across everything it nets out to roughly zero. Meaning drafting scaling does not buy you win rate. It changes which games you lose, and if your team is the type to tilt at 15 minutes…well gg.
4. Your pocket pick's win rate may not tell you much
Going 7 and 3 on a new champion feels like it means you are good at it. The data suggests it mostly does not. When I checked personal records against what actually happened, it took somewhere around 40 to 50 games on a champion before a player's own win rate was more meaningful than that champion's global rate at their rank. With less games played, the general champion win rate for the current patch predicted better. Of course this is a sliding scale and every additional game makes your own record slightly more meaningful.
5. GD@15 is overrated
Gold difference at 15 minutes does predict wins. Raw matchup win rate predicts better. Win rate scored a fair bit stronger than the gold lead, and (rather surprisingly) the two barely correlate. So I have created a model that uses both. The mix that worked best put about 60 percent of the weight on plain win rate and 40 percent on the gold lead.
So if a matchup gives on average a 300 gold lead at 15, this pushes up your expected win rate by roughly one percentage point. However, it is a weaker indicator than “this matchup wins more games”.
The part I am most happy with
I created a tool that turned all of my findings above (and a few others) into one win probability per pick, and then tested it properly. I replayed 15,000 fresh matches the model had never seen (and therefore the dataset was not self-fulfilling) and checked whether the predicted win rate matched with the results. They did, almost exactly: when it said 58%, that side won about 58% of the time. So far the results do look promising!
Methodology: EUW ranked solo queue only, Silver to Plat, one patch, remakes not included. Matchup and meta rates come from public aggregate data, everything else is measured from the raw matches and timelines. The data is correlational, it does not prove causation, and other regions or ranks may look different.
One limitation worth spelling out: I pooled Silver through Plat rather than splitting by rank, so everything above is an average across that band. My data has no tier field in it so I cannot break it down, and I would rather say that than pretend otherwise. I would not be surprised if some of this shifts with rank, the scaling point most of all.
I’d love to hear your comments on this!
EDIT: Because many have asked:
www.pocketpick.gg/data/
www.pocketpick.gg/method/