One Click matches players in time, space, and skill level. Most players enjoy close matches like 6-4, 6-7, 7-5 and despise lopsided matches like 6-0, 6-1. At One Click, we take pride in arranging close matches through a sophisticated rating system that we developed using over 7,000 matches played in the Sunnyvale tennis community.

One Click rating system places players on the scale 1.5 through 7 used by National Tennis Rating Program (NTRP) supported by United States Tennis Association (USTA) (Click here for details). We adjust ratings after every match arranged by One Click for which scores were reported.

The system is proven to be statistically accurate in predicting whether a given pair of opponents will play a close or lopsided match. For example, pre-match rating difference of less than 0.1 leads almost inevitably to a close match, while difference of more than 0.4 leads to an easy win by a higher rated opponent.

Your rating posted on the website reflects your performance in four recently played matches (or less than four if you just joined). The posted rating is a weighted average of raw ratings recorded after each of the recent matches. This is why in general the post-match rating transitions of both opponents may indicate the same direction of change. The raw rating transitions related to a specific match are always in opposite direction for each opponent, but the multi-match average may behave differently from the raw rating.

The calculation of raw ratings is, in a nutshell, based on the pre-match rating difference between opponents and the game-count match outcome to produce rating adjustments. The principal rating adjustments follow an empirically established curve that provides statistically expected raw rating difference for given match outcome.

Fig. 1: Example graph of function D=F(G)

The graph shows how difference D between ratings of match opponents statistically depends on parameter G that we call "games won ratio", the number of games won by the first opponent divided by the total number of games played in a match.

The actual "rating difference"-versus-"games won ratio" curve that we use has been determined by analyzing about 7,000 matches played by our community over past two years. We use separate curves for singles and doubles matches to reflect specific dynamics of each match type.

When two opponents play a match, we obtain the games won ratio G_m and calculate the match-based prediction of the rating difference D_m. This match difference may be different from the initial (pre-match) rating difference D_i. The discrepancy D_m-D_i indicates whether the ratings of the match opponents should be subject to adjustment. It also suggests the direction and magnitude of potential adjustments. The following example shows the idea of determining the adjustments.

Player rated 3.37 scored 7-6, 6-4 in a match with player rated 3.12. The games won ratio G_m for the match is (7+6)/(7+6+6+4) = .565. Suppose that the curve shown in Figure 1 gives D_m = 0.19 for G_m = .565. Pre-match rating difference D_i is 3.37 - 3.12 = 0.25. The rating difference discrepancy D_m - D_i is 0.19 - 0.25 = -0.06. The match outcome indicates that the opponents are closer to each other in rating than the pre-match difference D_i = 0.25 suggests. We want to reflect this observation by reducing the rating of 3.37 by a fraction of the discrepancy 0.06 while increasing the rating 3.12 by a fraction of the discrepancy 0.06. The actual fraction size depends on several factors mentioned below.

Please note that in this example the match winner's rating will go down while the match loser's rating will go up. This shows how the rating system works. You don't have to win matches to increase your rating. All you need is to perform better than the pre-match rating difference indicates based on our statistical model.

The principal adjustments derived from the "rating difference"-versus-"games won ratio" curve are subject to refinement by applying several empirically-established factors:

• Match score "strength" - the more games played in a match the more reliable indication of the skill difference between the opponents
• Randomness related to skill spread among players in doubles matches - the wider range of skills among a foursome the less predictable match outcome
• Patterns of individual performance fluctuations over time - this is based on the direction and variability of the rating adjustments recorded in the recently played matches
• Singles versus doubles match differentiation - the rating formula parameters differ between these for singles and doubles matches
• Compensation for the effect of closed community - the highest rated players of our community do not face opponents with rating higher than theirs; we slightly boost their rating adjustments to offset this phenomenon
• Slam win provision - the hardest case for any model-based rating is a match in which all games were won by one of the opponents; we make proper exceptions for such matches
• Initiation matches - the initial rating of self-rated players may be unreliable until they play a few "initiation" matches; we make provisions to avoid undesired impact of these matches on players with established ratings

Rating for New Players

Learning More about Ratings