Related Expert Tools
More precision tools in the video-games niche.
Encounter Calculator 5e
The Encounter Calculator 5e rates a Dungeons & Dragons combat encounter as Easy, Medium, Hard, or Deadly using the 2014 Dungeon Master's Guide XP threshold and encounter multiplier rules. Enter your party size and level, then add monsters by challenge rating, and the calculator converts CR to XP automatically rather than requiring you to look it up separately. It adjusts the encounter multiplier for unusually small or large parties.
HP Calculator 5e
The HP Calculator 5e works out a Dungeons & Dragons character's maximum hit points from class, level, and Constitution modifier, using either the fixed average method or manually entered rolls. It supports multiclass characters, applying each class's own hit die to the levels taken in that class. An optional Tough feat toggle adds the correct per-level bonus.
Impostor Odds Calculator
The Impostor Odds Calculator works out your exact probability of being assigned Impostor in a single Among Us lobby from the player count and impostor count, and separately calculates the binomial probability of landing a given number of impostor rounds across several games. It supports exact, at-least, and at-most probability modes for the multi-game calculation. Use it to see that each game's odds are completely independent of your recent run of luck.
Elo Rating Calculator Logic
Why Rating Gaps Don't Scale the Way People Expect
The most common misunderstanding I see is treating rating differences as a linear scale, where a 200-point gap should mean roughly twice the advantage of a 100-point gap. Elo's expected score formula is logistic, not linear, and the actual numbers diverge from that intuition quickly. A 100-point advantage gives an expected score of about 64%. A 200-point advantage gives about 76%, not 128%, since probability caps at 100%. A 400-point advantage gives about 91%, and even a massive 800-point gap only reaches about 99%, never quite hitting a certainty.
This matters most when interpreting what a rating change actually means. Beating a player rated 400 points below you barely moves your rating, because your expected score against them was already close to 1.0, so there was very little room to gain. Beating that same player when you are the lower-rated side, an upset, produces a much larger rating swing in your favour, since your expected score going in was low.
What the Elo Rating Calculator Actually Does
This calculator computes the two core Elo values for any single game: expected score, the probability-weighted result the rating system predicts before the game is played, and the resulting rating change once the actual result is known. According to Omni Calculator's Elo rating explainer, the system was originally developed for chess by Arpad Elo and has since been adopted across competitive gaming, from online chess platforms to matchmaking systems in video games.
Its sequence mode goes further than a single-game calculation: it tracks your rating through an entire series of games in order, since each game's expected score is calculated from your rating at that specific point in the sequence, not your original starting rating. This matters for anyone reviewing a tournament or a session's worth of ranked matches, where the rating used for game five depends on the outcomes of games one through four.
The Expected Score and Rating Change Formulas
Expected score for player A against player B is 1 divided by (1 plus 10 raised to the power of (opponent rating minus your rating) divided by 400). This produces a value between 0 and 1, interpreted as a win probability. The new rating is then the old rating plus the K-factor multiplied by the difference between actual score (1 for a win, 0.5 for a draw, 0 for a loss) and expected score.
The K-factor controls how much a single result can move your rating. According to A2Z Calculators' K-factor reference, a K of 40 is typically used for newer players still establishing an accurate rating (usually under 30 rated games), 20 for established players below 2400, and 10 for players who have reached 2400, since ratings at that level are considered stable enough that any single result should move it only slightly.
Why the Same Result Moves Ratings Differently
| Scenario | Expected Score | Result | Change (K=20) |
|---|---|---|---|
| Even match (0 gap) | 50% | Win | +10.0 |
| 200-point favourite wins | 76% | Win | +4.8 |
| 200-point underdog wins (upset) | 24% | Win | +15.2 |
| 200-point favourite loses (upset) | 76% | Loss | -15.2 |
Every one of these is the same K-factor and the same binary outcome, win or loss, yet the rating swing ranges from under 5 points to over 15, purely because expected score changes with the rating gap. This is the entire point of the system: it rewards results that were statistically unlikely and barely moves the needle on results that were already expected.
Accuracy and Limitations
This calculator's arithmetic matches the standard Elo formula exactly for the ratings, result, and K-factor entered. Real-world implementations vary in details this calculator does not model: some platforms use provisional ratings with wider uncertainty for new players, some apply rating floors or ceilings, and some (like Glicko or Glicko-2, used by several online chess platforms) add a ratings deviation and volatility component that Elo's original formula does not include.
The sequence mode assumes a fixed K-factor across all games in the series; if your actual K-factor changed partway through (for example, crossing the threshold from a new-player K of 40 down to 20 after 30 games), you would need to split the sequence into two separate calculations at that boundary for full accuracy.