Pdo S Sv Calculator
Free Pdo s% sv% Calculator for hockey. Enter your stats to get performance metrics and improvement targets. See charts, tables, and visual results.
Calculator
Adjust values & calculateOffense (Shooting)
Defense (Goaltending)
Formula
PDO combines team shooting percentage and save percentage on a 1000-point scale. A value of 1000 is league average. Higher values suggest hot shooting and goaltending, lower values suggest cold performance. PDO tends to regress toward 1000.
Last reviewed: December 2025
Worked Examples
Example 1: High PDO Team Analysis
Example 2: Low PDO Team Due for Improvement
Background & Theory
The Pdo (s% + Sv%) applies the following established principles and formulas. Sports statistics and performance metrics represent one of the most data-rich domains of applied mathematics available to the general public. Baseball, in particular, has developed an exceptionally dense vocabulary of calculated metrics. Earned run average (ERA) quantifies a pitcher's effectiveness as (earned runs × 9) / innings pitched, normalising performance to a nine-inning standard regardless of how many complete games were pitched. WHIP, or walks and hits per inning pitched, is computed as (walks + hits) / innings pitched and provides a complementary measure of how frequently a pitcher allows baserunners. Batting average, one of the oldest statistics in the sport, is simply hits / at-bats, though more modern metrics such as on-base percentage and slugging percentage have largely supplanted it as primary performance indicators. The NFL passer rating formula is considerably more complex, combining completion percentage, yards per attempt, touchdown rate, and interception rate into a composite score scaled to a 0–158.3 range. Golf handicap calculation, now governed by the World Handicap System introduced in 2020, uses a Handicap Differential formula applied to the best 8 of a player's most recent 20 score differentials, with adjustments for course rating and slope. The Elo rating system, originally developed by physicist Arpad Elo for chess ranking in the 1960s, has become a widely adopted framework for competitive ranking in sports ranging from football to table tennis. It updates each player's rating after every match based on the margin of expected versus actual result. In endurance sports, pace calculation converts total time to a per-mile or per-kilometre rate, informing training intensity and race strategy. In cycling, power-to-weight ratio (watts per kilogram) is the primary determinant of climbing performance and is central to both professional race analysis and amateur fitness tracking. Fantasy sports scoring systems synthesise multiple individual statistics into aggregate point totals, requiring participants to understand the relative value of different performance categories across sports.
History
The history behind the Pdo (s% + Sv%) traces back through the following developments. Organised athletic competition has roots extending to ancient Greece, where the Olympic Games were held at Olympia beginning around 776 BCE. These early games were embedded in religious observance and civic identity, featuring events such as sprinting, wrestling, and the pentathlon. The codification of modern sport rules accelerated dramatically in 19th century Britain, where industrialisation created both the leisure time and the institutional infrastructure for organised competition. The Football Association formalised the rules of association football in 1863, and similar governing bodies for cricket, rugby, tennis, and athletics followed in subsequent decades. Pierre de Coubertin, a French educator inspired by the English model of sport as character-building, campaigned to revive the Olympic Games as a modern international institution. The first modern Summer Olympics were held in Athens in 1896, establishing the template for international multi-sport competition that has continued to the present. FIFA, the international governing body for association football, was founded in Paris in 1904 with seven member nations. The serious statistical analysis of baseball, later termed sabermetrics, was pioneered by writers and analysts including Bill James beginning in the late 1970s. James self-published his Baseball Abstract annuals starting in 1977, introducing rigorous empirical methods to a domain previously dominated by traditional counting statistics and subjective scouting. His work influenced a generation of analysts and front-office executives. The publication of Michael Lewis's Moneyball in 2003, documenting the Oakland Athletics' 2002 season and their use of on-base percentage and other undervalued metrics, brought sports analytics to mainstream attention. The subsequent analytics revolution reshaped hiring practices and game strategy across professional sports leagues. Fantasy sports, which require participants to engage directly with statistical outputs, grew from a hobby practised by a few thousand enthusiasts in the 1980s into a multi-billion dollar industry by the 2010s, with tens of millions of participants across football, baseball, basketball, and other sports.
Frequently Asked Questions
Formula
PDO = (Shooting% x 10) + (Save% x 10)
PDO combines team shooting percentage and save percentage on a 1000-point scale. A value of 1000 is league average. Higher values suggest hot shooting and goaltending, lower values suggest cold performance. PDO tends to regress toward 1000.
Worked Examples
Example 1: High PDO Team Analysis
Problem: A team has scored 62 goals on 700 shots and allowed 40 goals on 680 shots over 20 games.
Solution: Shooting% = 62/700 = 8.86%\nSave% = (680-40)/680 = 640/680 = 94.12%\nPDO = (8.86 x 10) + (94.12 x 10)\nPDO = 88.6 + 941.2 = 1029.8\nDeviation from 1000 = +29.8
Result: PDO = 1029.8 (Running Very Hot -- expect regression toward 1000)
Example 2: Low PDO Team Due for Improvement
Problem: A struggling team: 38 goals on 720 shots, 55 goals against on 640 shots, 22 games.
Solution: Shooting% = 38/720 = 5.28%\nSave% = (640-55)/640 = 585/640 = 91.41%\nPDO = (5.28 x 10) + (91.41 x 10)\nPDO = 52.8 + 914.1 = 966.9\nDeviation from 1000 = -33.1
Result: PDO = 966.9 (Very Unlucky -- strong candidate for improvement)
Frequently Asked Questions
What is PDO in hockey and what does it measure?
PDO is a hockey analytics metric that combines a team shooting percentage (S%) and save percentage (SV%) into a single number, typically expressed on a 1000-point scale. The formula is PDO = (Shooting% x 10) + (Save% x 10), where 1000 represents the league average. PDO was named after the online alias of its creator, a hockey analytics blogger. The key insight behind PDO is that over large sample sizes, both shooting percentage and save percentage tend to regress toward league averages, meaning teams with extremely high or low PDOs are likely experiencing temporary luck rather than sustainable performance. It serves as a regression indicator.
Why does PDO always average out to 1000 across the league?
PDO must average to 1000 across the entire league because every goal scored by one team is simultaneously a goal allowed by another team. If one team shooting percentage goes up, another team save percentage goes down by the exact same amount. This mathematical certainty means that while individual teams can deviate significantly from 1000 in any given stretch, the league as a whole always averages exactly 1000. This zero-sum property makes PDO uniquely useful for identifying teams that are likely to regress, because extreme PDO values (above 1020 or below 980) are mathematically unsustainable for an entire season and almost always move back toward 1000.
What is considered a normal range for team PDO?
Over a full NHL season, most teams finish with PDOs between 985 and 1015, with the true average being exactly 1000. A PDO above 1020 is considered very high and strongly suggests the team is overperforming relative to their underlying play, while a PDO below 980 suggests significant underperformance. During shorter stretches of 10 to 20 games, PDO can swing much more wildly, with values above 1040 or below 960 not being uncommon. However, these extreme values virtually never sustain over a full 82-game season. The highest team PDOs in recent NHL history have been around 1035 to 1040 for a full season, which is exceptionally rare.
How can PDO help predict future team performance?
PDO is one of the most reliable regression indicators in hockey analytics. Teams with very high PDOs (above 1020) in the first half of a season tend to see their results decline in the second half as their shooting and save percentages return toward normal. Conversely, teams with very low PDOs (below 980) often improve significantly as luck evens out. Analysts use PDO alongside possession metrics like Corsi and Fenwick to separate genuine talent from temporary variance. A team with strong possession numbers but a low PDO is considered a prime candidate for improvement, while a team winning despite poor possession and high PDO is likely headed for a correction.
What is the difference between team PDO and individual player PDO?
Team PDO measures the combined shooting and save percentages of the entire team, while individual player PDO (sometimes called on-ice PDO) measures the shooting and save percentages that occur when a specific player is on the ice. Individual PDO can be influenced by the player linemates, opponents, and zone starts, making it more complex to interpret. Some elite players can sustain above-average individual PDOs because they genuinely elevate their team shooting percentage through skill. However, the save percentage component of individual PDO is largely outside a skater control, so extreme individual PDOs still tend to regress toward the mean.
Does PDO account for shot quality differences between teams?
No, standard PDO does not account for shot quality, which is one of its primary limitations. A team that generates high-quality chances from the slot will naturally have a higher shooting percentage, and a team with strong defensive structure may legitimately sustain a higher save percentage by limiting dangerous scoring opportunities. This means that some deviation from 1000 can be skill-based rather than luck-based. To address this, some analysts use expected goals (xG) based PDO, which adjusts for shot quality. Expected goals PDO compares actual goals to expected goals based on shot location and type, providing a more nuanced view of sustainability.
References
Reviewed by Sher, Sports Science & Nutrition Specialist · Editorial policy