Corsi Per Situation Calculator
Free Corsi per situation Calculator for hockey. Enter your stats to get performance metrics and improvement targets. Get results you can export or share.
Calculator
Adjust values & calculateEven Strength (5v5)
Power Play
Penalty Kill
Per-60 Rates
Corsi Differentials
Formula
Corsi For percentage divides shot attempts for by total shot attempts to determine possession share. Per-60 rates normalize raw counts by ice time, enabling fair comparison across different playing times and situations.
Last reviewed: December 2025
Worked Examples
Example 1: Even Strength Corsi Analysis
Example 2: Special Teams Corsi Comparison
Background & Theory
The Corsi (per Situation) 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 Corsi (per Situation) 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
CF% = CF / (CF + CA) x 100 | CF/60 = CF / TOI x 60
Corsi For percentage divides shot attempts for by total shot attempts to determine possession share. Per-60 rates normalize raw counts by ice time, enabling fair comparison across different playing times and situations.
Worked Examples
Example 1: Even Strength Corsi Analysis
Problem: A team generates 45 Corsi For and allows 38 Corsi Against at even strength in 48 minutes of 5v5 ice time. Calculate CF%, per-60 rates, and relative Corsi.
Solution: CF% = 45 / (45 + 38) x 100 = 45 / 83 x 100 = 54.2%\nCF/60 = 45 / 48 x 60 = 56.3 shot attempts per 60\nCA/60 = 38 / 48 x 60 = 47.5 shot attempts against per 60\nCorsi Differential = 45 - 38 = +7\nDifferential/60 = 56.3 - 47.5 = +8.8 per 60\nRelative Corsi = 54.2 - 50 = +4.2
Result: EV CF%: 54.2% (Above Average) | CF/60: 56.3 | Diff/60: +8.8
Example 2: Special Teams Corsi Comparison
Problem: On the power play: 18 CF, 4 CA in 6 min. On the penalty kill: 3 CF, 16 CA in 5 min. Evaluate both units.
Solution: Power Play CF% = 18 / (18 + 4) x 100 = 81.8%\nPP CF/60 = 18 / 6 x 60 = 180 per 60\nPP CA/60 = 4 / 6 x 60 = 40 per 60\nPP Rating: Elite\n\nPenalty Kill CF% = 3 / (3 + 16) x 100 = 15.8%\nPK CF/60 = 3 / 5 x 60 = 36 per 60\nPK CA/60 = 16 / 5 x 60 = 192 per 60\nPK Rating: Below Average
Result: PP CF%: 81.8% (Elite) | PK CF%: 15.8% (Below Average)
Frequently Asked Questions
What is Corsi and how is it used in hockey analytics?
Corsi is a shot attempt differential metric used in hockey analytics to measure puck possession and territorial control. Named after former NHL goaltending coach Jim Corsi, it counts all shot attempts directed at the net, including goals, saved shots, missed shots, and blocked shots. Corsi For (CF) represents shot attempts by a team or player when they are on the ice, while Corsi Against (CA) represents shot attempts by the opposition. The primary metric, Corsi For Percentage (CF%), is calculated as CF / (CF + CA) x 100. A CF% above 50% indicates the team is generating more shot attempts than they are allowing, which strongly correlates with possession time and territorial dominance. Corsi has become one of the foundational metrics in modern hockey analytics because it is a better predictor of future success than goals or even shots on goal.
Why is it important to separate Corsi by game situation?
Separating Corsi by situation (even strength, power play, penalty kill) is essential because each situation has fundamentally different characteristics. Even-strength play represents the majority of game time (approximately 80%) and is where team quality differences are most apparent because both teams have equal skaters. Power play Corsi is expected to be heavily skewed toward the team with the extra player, so a high PP CF% merely confirms normal advantages rather than exceptional play. Penalty kill Corsi is expected to favor the team at full strength, so achieving even moderate CF% while shorthanded indicates strong defensive play. Lumping all situations together produces misleading results because a team with lots of power play time will have inflated overall Corsi that does not reflect their even-strength competitiveness.
What is a good even-strength Corsi For percentage?
In the NHL, even-strength Corsi For percentage benchmarks are: below 45% is poor and indicates the team is being significantly outplayed in shot attempts, 45-48% is below average, 48-52% is average, 52-55% is above average, and above 55% is elite. Historically, the best possession teams in the NHL sustain even-strength CF% around 54-57%. During the dynasty era of the Los Angeles Kings (2012-2014), they maintained CF% above 55%, which was considered dominant. Individual player CF% is evaluated differently, as top defensive players might have lower CF% due to facing tougher opposition. Context like quality of competition, zone starts, and teammates all affect interpretation. A player with 53% CF% against top-line competition is more impressive than 55% CF% against weaker opponents.
How does Corsi per 60 minutes differ from raw Corsi numbers?
Corsi per 60 minutes (CF/60 and CA/60) normalizes shot attempt counts by time on ice, allowing fair comparison between players or teams with different amounts of playing time. A player who generates 30 Corsi For events in 20 minutes of ice time (90 CF/60) is more efficient at driving possession than one who generates 35 events in 25 minutes (84 CF/60). This rate-based metric is particularly important when comparing players across different roles, as top-line forwards typically play 18-22 minutes per game while fourth-line players might play 8-12 minutes. For team-level analysis, per-60 rates are valuable when comparing games of different lengths (regulation vs overtime) or when looking at specific situations where ice time varies significantly. The NHL average for even-strength CF/60 is approximately 55-60 shot attempts per 60 minutes.
What is relative Corsi and what does it measure?
Relative Corsi (Rel CF%) measures how much better or worse a team or player performs compared to a baseline, typically the 50% possession mark or the team average. For team-level analysis, relative Corsi = CF% - 50%, so a team with 54% CF% has a relative Corsi of +4.0. For player-level analysis, relative Corsi compares the individual to their team: Rel CF% = Player CF% - Team CF% without that player. A player with +3.0 Rel CF% improves their team's shot attempt differential by 3 percentage points when they are on the ice. This metric is valuable because it controls for team quality, identifying players who drive results regardless of their supporting cast. Elite possession drivers typically have Rel CF% of +3 to +6, while players who drag down their team's possession show negative relative Corsi values.
What are the limitations of Corsi as a possession metric?
While Corsi is valuable, it has notable limitations. Not all shot attempts are equal in quality since a shot from the slot is far more dangerous than a shot from the point, but Corsi counts both equally. This is why expected goals (xG) models have emerged as a complement to Corsi, weighting each shot attempt by its scoring probability. Corsi does not account for the score state effect, where teams that are trailing tend to generate more shot attempts (score effects) without necessarily playing better. Zone start effects can skew Corsi since players who frequently start in the offensive zone get inflated numbers. Blocked shots are counted as shot attempts, meaning a team that frequently blocks shots may appear to have worse Corsi despite effective defense. Finally, Corsi does not capture all elements of possession since a team can control the puck extensively without generating shot attempts.
References
Reviewed by Sher, Sports Science & Nutrition Specialist · Editorial policy