First Serve Percent Won Conversion

Reconcile actual first-in, conditional first-win and second-win tennis point evidence; first-in×conditional-win is not all service wins, no inferred double-fault policy.

Description

First-serve percentage and conditional first-serve wins use different denominators.

First-serve percentage and conditional first-serve wins use different denominators. Overall service wins must also include second-serve wins.^[1]

The returned values include First serve in percent, First serve conditional won percent, All service points won percent. The How section below explains the calculation, input rules and interpretation.

When to use First Serve Percent Won Conversion

Use First Serve Percent Won Conversion when you need to evaluate the supplied records under the calculation and evidence rules below. No double-fault classification or point reconstruction is supplied.

Prepare the observed race, match or training records and the stated units. Supply independently verified rule editions, calibration tables and targets where requested.

How to use First Serve Percent Won Conversion and understand its results

Prepare the observed race, match or training records and the stated units. Supply independently verified rule editions, calibration tables and targets where requested.
Enter observedServicePointCount, observedFirstServeInCount, observedFirstServePointsWon, observedSecondServePointsWon using the field table and calculation rules, then run the tool.
Read firstServeInPercent, firstServeConditionalWonPercent, allServicePointsWonPercent. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
observedServicePointCount	Observed service point count	number	Required
observedFirstServeInCount	Observed first serve in count	number	Required
observedFirstServePointsWon	Observed first serve points won	number	Required
observedSecondServePointsWon	Observed second serve points won	number	Required

Enter integer points 1–1000000, first-in count 1–points, first wins 0–firstIn and second wins 0–points−firstIn. Return the three percentages.

firstIn%=100×firstIn/points; firstWin%=100×firstWins/firstIn; totalWin%=100×(firstWins+secondWins)/points

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
firstServeInPercent	First serve in percent	number
firstServeConditionalWonPercent	First serve conditional won percent	number
allServicePointsWonPercent	All service points won percent	number

json

{
  "input": {
    "observedServicePointCount": 100,
    "observedFirstServeInCount": 60,
    "observedFirstServePointsWon": 42,
    "observedSecondServePointsWon": 20
  },
  "output": {
    "firstServeInPercent": 60,
    "firstServeConditionalWonPercent": 70,
    "allServicePointsWonPercent": 62
  }
}

Limitations and assumptions

No double-fault classification or point reconstruction is supplied.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Ace Rate Per Match Calculator: Aces per match and aces per hundred service points measure different exposures. Unequal match lengths affect the pooled rate. For a complementary task, use Unforced Error Differential Calculator: Unforced-error differential compares paired annotations. Positive values mean the player made more errors.

References

First Serve Percent Won Conversion — tool.io

Similar or alternative tools

Ace Rate Per Match Calculator
Aces per match and aces per hundred service points measure different exposures. Unequal match lengths affect the pooled rate.
Unforced Error Differential Calculator
Unforced-error differential compares paired annotations. Positive values mean the player made more errors.