Master+ EUW Ranked Reset Matchup Occurrence and Win Probabilities — All 63,504 ordered Matchups

5v5 matchups in the EUW Master+ pool of 31,000 players, split into six tiers by LP. Click any column header to sort, or filter by composition.
Methodology — how the win probabilities are calculated

The pool. 31,000 players: 80 HC (High Challenger, 2500+ LP), 220 LC (Low Challenger, 2000–2500), 700 GM (Grandmaster, 1500–2000), 1,500 HM (High Master, 1000–1500), 5,000 MM (Mid Master, 500–1000), 23,500 LM (Low Master, 0–500).

MMR per tier (centred on LM = 0): HC +1275, LC +1000, GM +750, HM +500, MM +250, LM 0. These are tier midpoints derived from the LP cutoffs — HC's midpoint is mass-weighted up to ~2800 LP because the tier extends to 3290. The MMR values are roughly half the LP differences because LP gains in Apex are inflated relative to underlying skill movement: Riot's baseline ±30 LP per win for a fairly-matched game implies ~10–15 MMR per win once you account for the LP/MMR reconciliation Riot applies at the top of the ladder (see Riot /dev: MMR-to-Rank Distribution on the +10/−30 phenomenon and WhatIsMyMMR on the rough 2:1 LP:MMR compression). Only differences between tiers matter for the math, not the absolute zero.

Team rating = soft-max with T = 400. Each team's rating is a soft-max (log-sum-exp) of its five players' MMRs: R_team = T · log(Σ exp(R_i / T)). T controls how much the strongest player dominates: at low T the team rating ≈ MAX, at high T it ≈ AVG. T = 400 was chosen so that 5 GMs are roughly 60% favourites against 1 HC + 4 LMs (at T = 300 the same matchup gives 5 GMs ~43%, at T = 500 ~66%) — coordinated GMs beating one carry surrounded by passengers, which matches high-elo intuition. T = 400 is also of the same order as the 500-MMR gap between adjacent tiers, meaning the strongest player only starts to dominate when they're a full tier or more above their teammates. Lower T (e.g. 200) would make a single Challenger essentially unbeatable; higher T (e.g. 800) would make them barely matter. The aggregator direction (lean toward MAX, not pure AVG) is supported by Dehpanah et al. (2021), who found MAX-style aggregation predicts MOBA outcomes better than SUM/AVG. The exact value T = 400 is calibration to feel, not measurement — no published value exists.

Win probability uses standard Elo with S = 400. Once both teams have a rating, win probability is the standard Elo logistic: P = 1 / (1 + 10^((R_opp − R_team)/S)). S = 400 is the chess default — it means a 400-point MMR gap makes the higher team 10× more likely to win. The full Master+ MMR range here (~1275 between HC midpoint and LM) is roughly comparable to the active range in chess, so the chess scale transfers directly. With S = 400, an HC vs LM 1v1 is 99.9% — appropriately one-sided without being absurd.

Match-occurrence probability. Assumes uniform random sampling of 10 distinct players from the 31,000-player pool, then split into two teams of 5. The "1 in X games" column is the reciprocal of this probability. This is most accurate in the first few games after the Apex hard reset, when everybody starts at Master 0 LP and Riot's matchmaker has no rank/MMR history to work from — so genuinely random pool draws are the realistic expectation. As players accumulate games, Riot's matchmaker progressively tightens around team-MMR balance and pulls every player's expected win rate toward 50%, which means the lopsided matchups in the bottom of the table become even rarer in practice.

Why 63,504 rows? There are C(10,5) = 252 distinct 5-player team compositions across 6 tiers, and 252² = 63,504 ordered matchups (Team 1, Team 2). Each non-mirror matchup appears twice — once from each side's perspective — so it's easy to look up "what's my win chance if I'm on this side."

HC LC GM HM MM LM
Team 1
Team 2
Type a number 0–5 in any tier box to filter; blank = match anything. Example: putting 0 in the Team 1 GM, HM, MM, and LM boxes shows only matchups with HC and LC players on Team 1.
Team 1 (blue) Team 2 (red) Zero (dimmed) High p_win Low p_win
Filtered totals
Avg P(T1 wins)
Combined occurrence
Matchups in filter

What's your expected win rate?

Enter your current LP (0 to 3290) and see your expected win rate against random matchmaking from this pool.
Expected win rate: