Here at 15-30 we have developed a simple, new tool to analyze matches, the serve factor. It combines 1st serve in % with 1st and second serve points won to give one metric to compare opponents.
Serve Factor = 1st Serve % (decimal) * 1st Serve + 2nd Serve
Where the 1st serve and 2nd serve are the percent of 1st serve and 2nd serve points won as a whole number.
It is simple to calculate, but powerful when contrasting two opposing players. The power comes from taking the differential of the serve factors for two players. Once enough points have been played so there is a sufficient set of data the serve factor differential tells you not only if a player is winning or will win but how decisively. The empirical results from using the serve factor during later rounds of the Australian Open 2020 and US Open 2020 are tabulated below.
| Serve Factor | Results |
| >30 | Decisive 3 set Victory |
| 20-30 | Tight 3 set Victory
OR
Decisive 4 set Victory |
| 10-20 | Tight 4 set Victory
OR
Decisive 5 set Victory |
| <10 | Tight 5 set Victory |
| Negative | *Rarely seen but can occur when several tie breakers, an injury, or a default decides a match |
The greater the margin of the serve factor differential the more dominant the victory. This makes sense because the more points a player can win on his own serve and the more return points he wins (thus hurting his opponent’s serve factor) the greater the odds are of a victory for a player.
The serve factor predicted the winner (in hindsight) of 10/13 matches of the US Open thus far.
The serve factor predicted the winner (in hindsight) of 13/15 matches of the 2020 Aussie Open (4th round-Final). One match that it failed to predict was the Thiem-Nadal match. The differential was Nadal by 5 points thus predicting Nadal would win in a tight 5 setter. In actuality, Thiem won in a tight 4 setter but there were 3 tie breaks and Thiem won all of them.
The other match it failed to predict was the Thiem-Zverev Semi-final. The differential was Zverev by 13 points thus predicting Zverev would win in a tight 4 setter. In actuality, Thiem won in a tight 4 setter but there were two tie breaks and Thiem won both of them. Moral of the story, never count Thiem out especially in tie breaks.
Serve factor’s weaknesses are that it doesn’t take into account tie breaks or break points specifically. The break point omission doesn’t hurt it as badly. Break points are “Built in” due to the measure of the effect a player has on hurting his opponent’s serve factor. But it doesn’t recognize a player who generates tons of break point chances but wastes nearly all of them or a player who gets only a couple break point chances all match but converts all of them.
Tie breaks are difficult because they are a smaller sample size of points and the serve factor model assumes the players continue to play at the same statistical rate in the tie break as they were during the rest of the match. Nerves, players mixing up tactics in the tie breaks, lucky shots, net cords, fatigue, all have a much bigger weight in a tie break than over the course of a set with many more points to spread the chaos over. The tie break can be over in the course of seven points. A seven point lapse in a set might not even necessarily lead to a break but a seven point lapse in a tiebreak is detrimental.
Overall, we think the serve factor is a powerful tool. Take the Medvedev-Rublev quarterfinal at this year’s US Open. Medvedev won in straight sets, two of them tiebreaks and in the first tiebreak he was down 5-1 and then 6-3. The serve factor differential was 16 to Medvedev, thus predicting Medvedev would win in a tight 4 setter. In actuality he ended up winning a tight three setter, but he was one point away from having to win it in 4 sets. All match Rublev was ruthless in his heavy baseline attack after hitting his first serve. The serve factor differential shows us that Medvedev might have gotten a little lucky with not having to play a 4th set. The serve factor overall, shows the quality of a tennis player on a given day.
Click here to view is our spreadsheet that tracks serve factors.
