u/MuffinCloud24

Does anybody else notice during peak times their metro north train just sits at a stop for 2 or 3 minutes before heading into GCT? I could understand if the train arrived early to a stop and it doesn’t want people to miss the stop before heading off, but we’re already running 1-2 minutes late and the train either sit at the last stop for a few minutes, or will mosey on into GCT through the tunnels at a glacial 5-15 mile an hour pace and my train that was either on time or was going to arrive 1-2 minutes late is now 5-6 minutes late.

It’s not all the time, of the last 9 rides in my last 3 weeks, 5 were late getting into GCT between 8 and 9am. Is it a capacity issue and the terminal can’t assign tracks quickly enough to arriving trains? Is my train getting delayed intentionally to make room for another train that was even more late?

It’s very frustrating losing 2-8 minutes half the time on a 38 minute ride (my 38 minute ride becomes 46 minutes). If I take the train 20 minutes earlier, it’s a 50 minute ride and only gets me into the station 8 minutes earlier which means I might still be marginally late for my 9am meetings. I’d have to commit to a train that’s 40 minutes earlier to guarantee being “on time” as opposed to being 4-5 minutes late 😡

Yes, AI helped me write this post and do the stats analysis. It’s smarter than I am. But I want the community’s thoughts on the points it raises and whether you may think differently. For instance, it seems like so many games are out of my control, but I do realize I could be more like TFBlade and hard carry 90% of games if I were better (but I’m not). So maybe I don’t deserve to play in games with the players that can 🤷🏼‍♂️. What do you think?

TLDR: because players have agency and smurfs / griefers can highly influence the outcome of a match, the match can’t be predetermined from the onset. Alternatively, because riot knows who griefers and smurfs are with accurate tools, they can engineer the outcomes of games to keep players engaged.

Points for disproving EOMM:

To test the theory that Riot Games forces game outcomes independently of individual player actions while maintaining a macro binomial distribution, we must evaluate the statistical property of individual player agency.
If the matchmaker predetermines outcomes to fit a perfect binomial distribution, a player's real-time performance (their "agency") becomes statistically irrelevant to the result. We can test this hypothesis by analyzing publicly available player data against specific statistical metrics.

1. The Smurf/Booster Calibration Test (The Ultimate Disproof)
If outcomes are entirely predetermined to fit a strict streak schedule, a highly skilled player placed in a lower-ranked lobby would still be forced to lose games when the algorithm schedules a "loss."
The Data Reality: Data from high-Elo players streaming "Unranked to Challenger" climbs or community auditing of booster accounts show initial win rates between 85% and 95% in lower brackets.
Statistical Meaning: If agency were zero, these players would hit the predetermined "loss" walls dictated by the binomial distribution. The fact that extreme skill variance completely overrides the expected distribution proves individual agency is a massive variable in the match outcome.

2. Performance Metrics and Outcome Correlation
If games are forced wins or losses regardless of your actions, your individual in-game performance metrics should decouple from the match outcome during forced streaks.
The Test: Tracking performance metrics—such as Gold Differential at 15 minutes (GD15), Kill Participation (KP), and Death Share—across thousands of matches.
The Statistical Finding: Public data aggregators show a massive, statistically significant correlation between a player's early-game metrics and their final win/loss result. Players who actively secure a positive GD15 win substantially more often than a predetermined system would allow. If outcomes were fixed, high-performing individuals would still suffer a 50% drop-off during "forced loss" phases, which the data does not show.

3. The "AFK / Griefing" Baseline
The theory suggests that an outcome occurs "absent any action on their behalf." We can statistically test the exact opposite: what happens when a player actively tries to force a loss, or goes completely idle?
The Data Reality: Statistics tracking games with an early AFK player or an active "inter" show that the compromised team's win rate plummets to below 10–15%.
Statistical Meaning: If the algorithm had pre-decided that the match was a "forced win" for that team, the system would have to overcompensate by making the remaining 4 players impossibly strong, or forcing the enemy team to disconnect. Because an active negative action almost guarantees a loss, it proves that individual agency directly dictates the boundaries of the outcome.

4. How the Illusion Occurs: High-Variance Matchmaking
How does a system feel predetermined while remaining statistically fair? The answer lies in match variance, not forced outcomes.
To keep queue times low, matchmaking algorithms create a "match balance" using average MMR. A lobby might look like this:

Team A (Average MMR: 1500)
Team B (Average MMR: 1500)
Player 1: 1700 MMR (High Skill), paired with Player 2: 1300 MMR (Low Skill)

Player 6: 1500 MMR (Average) paired with Player 7: 1500 MMR (Average)

In this scenario, the matchmaker mathematically rates this as a 50/50 game (fitting the binomial model). However, to win, Player 1 must carry Player 2. If Player 1 plays at an average level, they will lose. This creates the psychological illusion of a "forced loss"—it feels like nothing you did mattered, but in reality, the match simply demanded a level of agency higher than your current baseline to tip the scale.

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Argument for how EOMM can exist even with the lack of macro distribution stats to support the claim, and that there are demonstrable examples of how players can influence the outcome of every game (smurfs and griefers):

Looking strictly at the mechanics of database sorting and predictive algorithms, it is entirely mathematically possible to force outcomes for the other 9 players using those tools. [1]
By isolating the "smurf" as a 90% win-rate statistical anomaly, the system does not need to guess the outcome. It can weaponize that anomaly like a chess grandmaster placed into a room of amateurs, effectively dictating whether the other 9 players win or lose. [1, 2]
If an algorithm wanted to engineer a forced win or loss for a target group of players using these exact metrics, it would execute the following steps:

1. The "Anchoring" Method (Engineered Loss)
To force a loss for a target player (or group of players), the matchmaking system can intentionally create an imbalanced social and skill dynamic. [1, 2]
The Formula: Take the 90% win-rate smurf account and place them on Team A. [1]
The Balancing Act: To keep the average MMR of both teams looking perfectly identical on paper (to satisfy public data auditors), the algorithm pairs that smurf with 4 players flagged by the behavioral system as highly tilted, low-honor, or on severe loss-streaks. [1]
The Consequence for Team B: The 5 players on the opposing team are completely ordinary, average-elo players. Because the algorithm knows the 90% win-rate smurf will overwhelmingly override the low-performing teammates, the 5 players on the opposing team have been handed a mathematically predetermined loss, regardless of their independent choices.

2. The "Quarantine Trap" Method (Engineered Win)
Conversely, the matchmaker can use a smurf to artificially hand a win to players it wants to keep engaged (e.g., players on the verge of quitting after a massive loss streak). [1]
The Formula: The system identifies a target player who has a low honor rating or is highly tilted but needs a win to prevent them from closing the client. [1]
The Balancing Act: It places that tilted player directly onto the smurf's team.
The Consequence: The smurf "1v9s" and carries the match. The tilted player gets a "forced win" they did not earn, while the 5 players on the opposing team are subjected to a "forced loss" purely to serve as the engagement collateral for the tilted player's retention loop. [1]

Why This Aligns with Riot's Public Patents
This exact type of orchestration is documented in public patent filings. Publicly available legal documents reveal that Riot holds patents for systems that explicitly dynamically match players based on personality scores, behavioral history, and social dynamics (such as mixing a designated "Leader" profile with specific "Teammate" profiles) rather than raw skill numbers alone. [1, 3, 4]
If the system has the capability to analyze how often a player tilts, how they chat, and how they react to loss streaks, it possesses all the necessary data points to treat a smurf account as a literal lever to tilt the scale for the other 9 profiles in the lobby.