Frequency

Frequency in basketball analytics refers to how often a player, team, or action occurs, typically expressed as a rate, percentage of total possessions, or attempts per game. This fundamental statistical concept provides crucial context for understanding shooting patterns, play type usage, tactical tendencies, and player roles that efficiency metrics alone cannot reveal. Frequency data transforms basketball analysis from simple success rate evaluation to comprehensive understanding of volume, role, and strategic emphasis. Modern tracking systems enable precise frequency measurement across numerous dimensions including shot location frequency, play type frequency, defensive coverage frequency, and movement pattern frequency, making frequency analysis essential for player evaluation, scouting preparation, and strategic optimization. The conceptual importance of frequency alongside efficiency recognizes that high efficiency on rare attempts provides less value than moderate efficiency on high volume. A player who shoots 60 percent from the restricted area but only attempts one shot per game from there generates minimal offensive value from that efficiency. Conversely, a player shooting 42 percent from three on 8 attempts per game creates substantial value through high-volume efficient shooting. Combining frequency with efficiency through metrics like points per game from specific locations or total expected points reveals complete offensive contributions. Shot location frequency measures how often players attempt shots from various court areas, typically expressed as percentage of total field goal attempts or absolute attempts per game. A modern three-point specialist might show 60 percent three-point frequency (60 percent of attempts from beyond the arc) with 30 percent restricted area frequency and only 10 percent mid-range frequency. This frequency distribution reveals shot selection patterns and offensive role. Comparing individual frequency patterns to team or league averages identifies players who follow or diverge from modern analytical orthodoxy favoring threes and layups over mid-range. Three-point frequency has become one of the most important shooting statistics in modern basketball, measuring what percentage of a player's field goal attempts come from beyond the arc. League-wide three-point frequency has increased dramatically from under 20 percent in the 1990s to over 40 percent in the modern NBA, reflecting strategic evolution. Individual three-point frequency ranges from over 70 percent for pure spot-up shooters to under 10 percent for traditional centers. High three-point frequency combined with good efficiency indicates valuable modern offensive skills. Rim frequency quantifies what percentage of attempts occur at the rim (restricted area), typically the most efficient shot location due to close proximity. Athletic wings and guards who penetrate effectively show 40-50+ percent rim frequency through drives and cuts. Centers who primarily operate near the basket show 60-80+ percent rim frequency. Perimeter-oriented players who struggle finishing or getting to the basket show low rim frequency under 20 percent. High rim frequency often correlates with efficient scoring due to layup/dunk advantages, making it a valuable offensive attribute. Mid-range frequency measures what percentage of attempts come from two-point shots outside the paint, the least analytically favored shot type due to lower expected value than threes (same point value as closer twos but harder to make). Modern analytically-informed players show declining mid-range frequency, many under 15 percent as they emphasize threes and rim attempts. Traditional mid-range specialists like DeMar DeRozan maintain 40+ percent mid-range frequency, requiring exceptional mid-range efficiency to justify this shot distribution. Excessive mid-range frequency often indicates inefficient shot selection absent elite skill. Play type frequency data from tracking systems like Second Spectrum quantifies how often players engage in specific offensive actions: isolation, pick-and-roll ball handler, pick-and-roll roll man, post-up, spot-up, off-screen, cutting, hand-off, transition, etc. This frequency distribution reveals player role and offensive responsibilities. High pick-and-roll ball handler frequency indicates primary playmaking duties. High spot-up frequency suggests off-ball shooting role. Understanding play type frequency enables evaluating whether players excel in their most frequent actions or struggle despite high usage. Isolation frequency measures what percentage of a player's possessions involve isolation plays where they attack a single defender with limited ball movement or screening. Elite shot creators like Luka Doncic and James Harden show isolation frequency of 15-25+ percent, while role players typically stay under 5 percent. High isolation frequency requires elite shot creation ability to maintain efficiency, as isolation generally produces lower quality shots than ball movement offense. Excessive isolation frequency can indicate offense stagnation absent elite individual talent. Pick-and-roll frequency quantifies involvement in basketball's most common offensive action, measured separately for ball handlers (who dribble around screens) and roll men (who screen and roll to the basket). Elite pick-and-roll ball handlers like Chris Paul and Damian Lillard show 25-35+ percent frequency as ball handlers. Rim-running centers like Clint Capela show high roll man frequency. Pick-and-roll efficiency combined with frequency reveals who creates the most value from this foundational action. Post-up frequency has declined league-wide as modern basketball de-emphasizes traditional post play in favor of spacing and three-point shooting. Most modern players show post-up frequency under 5 percent. Traditional post players might show 20-30+ percent post-up frequency, requiring strong post efficiency to justify this volume given generally lower expected value compared to threes and transition. Declining post-up frequency represents one of the clearest indicators of modern strategic evolution. Transition frequency measures what percentage of possessions occur in transition (fast break opportunities before defenses fully set). Teams emphasizing pace and athleticism show transition frequency of 18-22+ percent, while slower half-court oriented teams stay under 14 percent. Players who excel in transition should play for up-tempo teams maximizing their transition frequency and value. High transition frequency often correlates with offensive efficiency given transition's expected value advantages. Catch-and-shoot frequency quantifies what percentage of a player's shot attempts come from catch-and-shoot opportunities (shots taken within 2 seconds of receiving a pass with 2 or fewer dribbles). Pure spot-up shooters show catch-and-shoot frequency above 80 percent, while primary ball handlers might be under 20 percent. High catch-and-shoot frequency indicates off-ball offensive role, while low frequency suggests shot creation responsibilities. Given catch-and-shoot efficiency advantages, high frequency in this shot type is generally desirable. Pull-up frequency measures what percentage of attempts involve pulling up off the dribble (possessing the ball 2+ seconds before shooting). Elite shot creators show pull-up frequency of 50-70+ percent, demonstrating self-creation responsibilities. Role players typically show pull-up frequency under 30 percent, emphasizing catch-and-shoot attempts instead. High pull-up frequency requires maintaining efficiency despite the difficulty of these attempts, as pull-ups succeed at lower rates than catch-and-shoot shots from similar locations. Drive frequency quantifies how often players attack the basket off the dribble, measured as drives per game or drives per possession. Penetration-oriented guards and wings average 10-20+ drives per game, creating rim attempts, free throws, and kick-out passing opportunities. Post-oriented big men and pure spot-up shooters show minimal drive frequency under 3 drives per game. High drive frequency creates offensive value through direct scoring, free throws, and playmaking opportunities, making it a valuable skill for perimeter players. Contest frequency on defense measures how often defenders contest opponent shots, indicating defensive engagement and closeout discipline. Elite perimeter defenders contest 65-75+ percent of opponent three-point attempts in their vicinity, showing excellent closeout execution and effort. Poor defenders might contest under 50 percent of opponent attempts, conceding open looks. High contest frequency combined with low opponent shooting percentage when contested indicates elite defensive impact. Screen frequency measures how often players set screens, quantified as screens per game or screen assists (shots made shortly after screens). Elite screeners like Steven Adams and Draymond Green set 5-10+ screens per game resulting in shot attempts. Screen frequency reveals offensive role beyond box score statistics: players who screen frequently create offensive value through teammate shot creation even without recording assists. Combined with screen quality metrics, screen frequency helps evaluate screening contributions. Touch frequency quantifies how often players possess the basketball, measured as touches per game. Primary ball handlers show touch frequency of 70-100+ per game, indicating high usage and playmaking responsibilities. Spot-up role players might average 30-40 touches per game in limited offensive involvement. Touch frequency combined with time of possession reveals playing style: some players make quick decisions with brief touches, while others dominate possession with extended touches. Help rotation frequency on defense measures how often players leave their primary assignment to provide help defense, indicating defensive activity and awareness. Versatile help defenders show high rotation frequency, actively supporting teammates. Less engaged or limited defenders show lower rotation frequency. However, excessive help frequency can indicate over-helping that creates open shots for opponents, so optimal frequency balances help provision with assignment responsibility. Switching frequency measures how often defenders switch assignments, typically during screen defense. Modern switch-heavy defenses show switching frequency of 60-80+ percent on ball screens, while traditional drop coverage teams might switch under 30 percent. Individual switching frequency reveals role in team defense: versatile defenders who can guard multiple positions show high switching frequency, while specialized defenders with limited versatility switch less frequently. Temporal frequency patterns reveal strategic adjustments across game situations. Teams might show different shot location frequency in transition versus half-court, early shot clock versus late clock, regular season versus playoffs, or leading versus trailing. These context-dependent frequency changes indicate tactical flexibility and situation-appropriate strategy. Understanding frequency patterns across situations enables predicting opponent tendencies and optimizing tactical responses. Frequency stability across time affects strategic reliability. Frequency patterns sustained over multiple seasons reflect genuine tendencies suitable for long-term strategic planning. Single-season frequency anomalies might represent role changes, injury effects, or coaching emphasis shifts. Multi-year frequency data provides reliable foundations for scouting and personnel decisions. Opponent-adjusted frequency reveals whether usage patterns change against different defensive qualities. Some players maintain frequency patterns against all opponents, while others show frequency changes against elite defenses (forced away from preferred actions) or poor defenses (increased aggressive attacking). Understanding opponent-adjusted frequency informs expectations about performance against quality competition. Frequency optimization balances efficiency and volume to maximize total production. Simply maximizing frequency of highest-efficiency actions can be counterproductive if defenses adjust, diminishing efficiency as frequency increases. Optimal frequency often involves diverse action types preventing defensive overloading of any single tendency. Analytical optimization seeks frequency distributions maximizing total expected points across all action types. Limitations of frequency analysis include inability to capture shot quality within categories, missing information about defensive context causing frequency patterns, and potential confounding from teammate effects. Frequency alone doesn't indicate whether attempts are contested or open, skillfully created or system-generated. Defensive schemes might force certain frequencies, making them reflect opponent tactics rather than player preference. Teammate floor spacing can enable frequency patterns that wouldn't be sustainable with different surrounding personnel. The future of frequency analysis will likely involve more granular conditional frequencies accounting for game context, defensive coverage, teammate positioning, and sequential play patterns. Machine learning could identify optimal frequency patterns maximizing efficiency for individual players and team compositions. Real-time frequency tracking could enable in-game tactical adjustments based on observed opponent frequency patterns. In contemporary basketball analytics, frequency provides essential context transforming efficiency metrics into comprehensive production evaluation. Combined frequency-efficiency analysis reveals complete offensive contributions, defensive tendencies, and strategic patterns that either metric alone cannot capture. Frequency data guides shot selection optimization, role definition, scouting preparation, and tactical strategy across modern basketball, making it indispensable alongside efficiency for understanding player value and team performance.