Team 158 [cognitiveclock.com](https://www.google.com/url?q=http://cognitiveclock.com&sa=D&source=editors&ust=1759836858712217&usg=AOvVaw2RwaaINdYJ9yUlK-pvXNBm) # Abstract Traditional defensive metrics in baseball excel at quantifying physical outcomes but fail to capture the subconscious, split-second mental processing that precedes them. This paper introduces the Cognitive Clock Score (CCS), a metric framework designed to objectively measure a player's cognitive performance during the pre-acquisition stage of a defensive play: the critical process from bat-on-ball contact to securing the ball in the glove. The data is displayed in a dynamic, lightweight web app available at [cognitiveclock.com](https://www.google.com/url?q=http://cognitiveclock.com&sa=D&source=editors&ust=1759836858713267&usg=AOvVaw3dFbwNsJ0ZVxmcUJc7xu6Y)  Leveraging SMT’s tracking data, the CCS deconstructs plays into five core cognitive components: Reaction Time (RT), Path Efficiency (PE), Movement Decisiveness (MD), Adaptability (AD), and Anticipation (AN). These metrics are calculated and contextualised via a data pipeline that normalises performance against league positional benchmarks. In addition to creating a 20-80 score for each factor, multi-layered Cognitive Archetypes are generated for  Players. This framework translates complex quantitative scores into intuitive narratives, identifying players with archetypes such as "Thinkers," "Reactors," or "Hesitant Processors" and detailing specific trade-offs such as "Explosive but Inefficient" or “Chaotic Creator”. The result is a powerful tool that complements traditional eye tests, providing an empirical foundation for player evaluation, targeted development, and strategic team construction.  # Table of Contents [Abstract        2](#h.8nwmd7e4wlak) [Table of Contents        3](#h.u6f2ihdlzzip) [1. Introduction: The Unseen Game        4](#h.jesdtf6k1xru) [2. Methodology Part I: The Anatomy of a](#h.54q2sj7tw4i6) [Decision](#h.54q2sj7tw4i6)        [5](#h.54q2sj7tw4i6) [4. Analysis & The CCS Portal](#h.a1bypp4na1d2)[:](#h.a1bypp4na1d2) [Where Data Becomes a Story        8](#h.a1bypp4na1d2) [5. Discussion](#h.l7tzxqvfukvf)[:](#h.l7tzxqvfukvf) [Trends and Methodological Considerations        10](#h.l7tzxqvfukvf) [6. Conclusion](#h.6eri4t95w5p5)[:](#h.6eri4t95w5p5) [Applications and Strategic Value        11](#h.6eri4t95w5p5) [Acknowledgments](#h.efoxp01ec2kb)        [](#h.efoxp01ec2kb)[12](#h.efoxp01ec2kb) [Citations        12](#h.r415y1z0zh5i) [Appendix](#h.jr6vuxwcxkar)        [13](#h.jr6vuxwcxkar) --- --- # 1. Introduction The Unseen Game A line drive is scorched into the gap between second and third base. In less than a second, the shortstop explodes from his ready stance, dives horizontally, and snares the ball just before it touches the grass. Replays will celebrate the spectacular physical achievement: the extension, the timing, the athleticism. The entire sequence, from the bat’s crack to the ball meeting leather, might take only a second. Yet this outcome is governed by a cascade of subconscious decisions made in the milliseconds after contact. How did the player’s mind process a chaotic event into such precise, purposeful action? How does the brain's processing speed dictate the success of the entire second-long journey? This is the unseen game. Modern analytics, like Baseball Savant, provide an incredible, yet fragmented, view of this process. Existing high-fidelity data can isolate physical events with precision: quantifying velocity, sprint speed, or the efficiency of a route, with metrics like [Jump](https://www.google.com/url?q=https://baseballsavant.mlb.com/leaderboard/outfield_jump&sa=D&source=editors&ust=1759836858719020&usg=AOvVaw3414wgBYxmZAz61jPClSxx). Meanwhile, combination metrics currently employed  (e.g., [Fielding Run Value](https://www.google.com/url?q=https://baseballsavant.mlb.com/leaderboard/fielding-run-value&sa=D&source=editors&ust=1759836858719331&usg=AOvVaw1CmARezOQNukdeIZIeFyTa)) are predominantly focused on the outcome of the play.  The result is that these metrics tell a story of what happened, but they often struggle to explain why in a unified way. They provide the individual puzzle pieces of a defensive play without a framework to assemble them. A scout can see a slow first step, a poor route or a bad outcome, but must intuitively guess if the cause is a physical limitation or a series of cognitive miscalculations. The Cognitive Clock Score (CCS) is designed to bridge this gap. This paper introduces CCS as a holistic framework that synthesises physical and positional data into a unified narrative of a player’s pre-acquisition phase. By measuring and weighting the underlying cognitive components of defensive plays across the season, the CCS moves beyond describing isolated actions to build a comprehensive profile of a player’s defensive mind. --- # 2. Methodology Part I  The Anatomy of a Decision The Cognitive Clock Score (CCS) is a composite metric built upon five distinct cognitive pillars. Each pillar is [independently measured and designed](#h.tk4985pld8jn) to answer a fundamental question a scout might ask when evaluating a player's defensive instincts. - Reaction Time (RT): How explosive is the first move? This metric measures pure neuromuscular response, calculating the time a player takes from bat-on-ball contact to their first significant movement. This isolates the primal burst of a player's reaction from more complex decision-making, representing the raw speed connecting perception to action. A lower raw value signifies elite reflexes   - (RT = t_first_move - t_stimulus). - Path Efficiency (PE): Does the player see the right line to the ball? This ratio metric quantifies innate spatial awareness by comparing the straight-line distance to the ball's destination with the actual distance the player travelled. It answers whether the player instinctively understands the most economical route to the intercept point. A higher ratio, approaching 1.0, reflects flawless route planning  - (PE = d_optimal / d_actual). - Movement Decisiveness (MD): Does the player trust the read and commit? This component scores cognitive commitment and movement quality, penalising hesitation in the form of abrupt decelerations or movement far below a player's peak speed for that play. It separates players who move with fluid and confident purpose from those who are tentative. A higher score indicates confident motion  - (MD ≈ 1 / (1 + ρ_hesitation)). - Adaptability (AD): How does the player react to the unexpected? Evaluates real-time problem-solving, measuring the time delay between an unexpected deviation in the ball's trajectory (e.g., a bad hop or a bobble) and the player's corresponding physical adjustment. It provides a true test of cognitive agility. A lower value signifies superior adaptability. - (AD = t_adapt-t_disruption). - Anticipation (AN): Did the player see the play coming? Unique among the components, this metric evaluates pre-play strategy by scoring how close a player's starting position is to the ball's actual destination. The optimal spot is determined by a simple model accounting for ball trajectory. It rewards players who shrink the field before the ball is even hit. A higher score reflects elite pre-play awareness  -  (AN ≈ 1 - (d_start→land / d_max)). Together, these five components form a holistic cognitive fingerprint. The Individual Metrics and the combined overall score follow the traditional [20-80 scouting scale](#h.eugfpoj6egbl), which allows for easy comparison and analysis. As shown in the CCS Radar Chart, the unique shape of a player's profile can reveal far more than a single aggregate score, illustrating the specific mental strengths and weaknesses that define their game. ---  3. Methodology Part II The Data Pipeline The CCS is powered by a robust, [three-stage pipeline written in R.](#h.dla9pgpbp66d) This modular approach ensures each stage of data processing is independent and auditable, systematically converting raw tracking data into a comprehensive profile, as shown in the flowchart. The process begins with 'The Funnel' script, which ingests raw player and ball tracking data to distil chaotic defensive plays into clean, raw metrics. Next, 'The Wrangler' provides essential context by calculating statistical norms for the metrics across league and position groups, creating a statistical rulebook. The final synthesis occurs in 'The Factory', which normalises a player's raw scores against these benchmarks and converts them to a traditional 20-80 scouting scale, where ten points is equal to one standard deviation. These component scores are then weighted based on specific positional demands to calculate a final Position-Adjusted CCS. This final stage of 'The Factory' script transitions from pure analytics to narrative insight through [archetype generation](#h.o6huez2ljtq3). This process translates the quantitative scores into a rich, multi-layered identity, making a player's cognitive profile instantly understandable. The system builds this narrative layer by layer: firstly assigning a Foundational Profile based on the overall CCS, categorising a player's general quality (e.g., 'Strong Cognitive Tools'). It then identifies a primary Style Profile, such as 'Thinker' or 'Reactor,' based on the dominant patterns among the five components. Finally, more specific narrative tags describe complex trade-offs ('Explosive but Inefficient') and flag individual skills as assets or areas of concern.  --- #  # 4. Analysis & The CCS Portal Where Data Becomes a Story To bridge the gap between data and decision-makers, the Cognitive Clock ([cognitiveclock.com](https://www.google.com/url?q=http://cognitiveclock.com&sa=D&source=editors&ust=1759836858730633&usg=AOvVaw0C8JTj4OOlkoB_Y_tLWtv6)) portal was developed. This interactive web application translates the raw numerical output into clear, actionable visual profiles. Despite dealing with anonymised players codes and complex data, the portal helps create the full dynamic profile, which provides end-user utility. Clicking the Tour button on the above link will visually take you through the tools and results available through the portal.The Player Profiles combine both the numerical scores and archetypes to display the unified narrative of a player’s pre-acquisition phase. The Full player profile additionally includes the raw scores, percentiles and combined averages for easy viewing of stats. The use of archetype tags and visual flags on profiles aids in the instant breakdown of a player's best/worst skills and the identified traits.  A simple natural language generator then synthesises the assigned archetype tags into concise performance summaries. The result is a visual cognitive profile that is both holistic and detailed. Users can immediately identify elite profiles, like Right Fielder AKX-1435 (72.9 CCS), a "Cognitive Superstar", or sort for specific tools and positional groups, such as identifying the league's best Outfielder or the most explosive first step by ranking players on Reaction Time ---  The true diagnostic power, however, emerges when analysing a player's Cognitive Fingerprint. The direct player comparison tool crystallises these narratives by pitting players' cognitive styles against one another. Here is one such nuanced example:  Player A: UPU-2411 (SS): An “Explosive but Inefficient" Mover. His cognitive fingerprint showcases a dramatic trade-off in execution. He possesses elite Movement Decisiveness with a score of 65.13 (99th percentile for his position) and Adaptability with a score of 61.1 (90th percentile for his position), but pairs this with a significant deficiency in Path Efficiency, scoring just 38.38 (2nd percentile). Player B: AVV-1517 (SS): An "Erratic Performer” hamstrung by indecision. His profile is the inverse of Player A's. He has elite Path Efficiency at 61.48 (97th percentile) and strong Anticipation, yet he is crippled by a low Movement Decisiveness score of 29.25 (2nd percentile). The story becomes instantly clear through their opposing profiles. Player A: UPU-2411 is the raw, confident athlete who commits to his path with explosive conviction and can react on the fly, but his poor read of the play results in a highly inefficient route. Player B: AVV-1517 is the cerebral technician who perfectly maps out the most efficient route to the ball but hesitates at the critical moment, lacking the decisive burst to execute the play and respond in the moment. For a coach, this presents a clear choice: invest in the raw explosive talent who reacts quicker and more decisively, and who might be coached into better routes, or the high-IQ player whose physical ceiling, confidence and execution ability might be lower. The CCS framework makes this trade-off explicit. # 5. Discussion Trends and Methodological Considerations The application of the CCS framework across the dataset reveals distinct cognitive patterns and provides initial validation for its underlying architecture. The prevalence of the "Erratic Performer" archetype, even among players with respectable overall scores, indicates that cognitive ability in baseball is rarely a case of uniform excellence; more often, it is a game of trade-offs, especially at a Minor League level. Further analysis of the cognitive components verifies how the framework's archetypes cluster across different phases of cognition. By plotting a player's Pre-Play Cognition score (their Anticipation) against their average In-Play Cognition score (a composite of RT, PE, MD, and AD), the archetypical clusters begin to become more visible.  ---  "Thinkers" (orange triangles) dominate the right side of the chart, validating their reliance on elite pre-play Anticipation. Conversely, "Reactors" (blue circles) cluster towards the top, showcasing their strength in in-play processing speed. The coveted "Complete Processors" (green squares) populate the top-right quadrant, successfully combining both skill sets. Meanwhile, profiles like "Hesitant Processors" and "Erratic Performers" occupy the lower quadrants, confirming that a deficiency in one or both cognitive phases is identified by the system. This visualisation serves as a strong validation, demonstrating that the CCS framework successfully categorises players into distinct, meaningful, and data-driven cognitive profiles. While the CCS Framework in its current form is able to robustly calculate the 5 individual scores and the overarching metric, the CCS from formulas through to observations still needs refining and the input of wider baseball experts.  The current dataset of a single abridged Minor League means that the data is tailored to these specific game speeds and player tendencies. The small sample of plays required the use of broader positional groups for comparison, rather than more precise, specific positions. A confidence score was used to attempt to visualise these varying sample sizes and their potential impacts on accuracy. Computational limitations also necessitated the removal of battery analysis. Finally, the current use of hard thresholds for archetype gating and the static positional weightings are identified as areas for future improvement through more sophisticated statistical modelling and regression analysis against play outcome data. # 6. Conclusion Applications and Strategic Value The Cognitive Clock Score’s architecture as a flexible framework provides a foundation for deeper, more dynamic analysis that supplements traditional eye tests. Its component-based nature allows for the dissection of individual plays to diagnose the specific cognitive breakdown or triumph in a key moment, offering granular feedback. The true value of the Cognitive Clock Score, beyond identifying trends and limitations, lies in its direct application to player improvement and organisational strategy. The secondary value of the framework lies in its power to generate new lines of analysis, providing the diagnostic foundation for new data-driven coaching development: future research could determine the predictive power of CCS for skill stickiness for minor league call-ups, or track how cognitive profiles change over a player’s career. The applicability of the framework translates directly into organisational strategy. For scouts, it offers a way to identify undervalued players whose cognitive gifts are not captured fully by current metrics. For player development, it provides a precise roadmap for transforming weaknesses into strengths through targeted, data-driven interventions. For the front office, it enables the construction of more synergistic defensive rosters, for example, blending the pre-play intelligence of "Thinkers" with the dynamic problem-solving of "Reactors." By quantifying the pre-acquisition phase and mapping the unseen game, the CCS framework provides the lens to shift the conversation about defence: from what a player did, to a deeper understanding of how they think. --- # Acknowledgments The data used in this project was provided by SMT. Conceptual and editing support from Dr Meredith Wills. Demo code, editing and conceptual support from Billy Fryers. Animation code derived from David Awosoga. Demo code from Andrew Steenkamer. Dr Mike Jensen for his input and coding advice. Luca Albertoni for conceptual support. Michelle Chang and Susan Millar for helping with all of it. The many Previous SMT Submissions which helped with inspiration, code and analysis. # Citations Cerenzio, B. (2025). Evaluating outfielder route efficiency and paths by level [Paper presentation]. SABR Analytics Conference. Dewan, J. (2013). A manifesto for defensive baseball statistics. Baseball Research Journal, 42(2). https://sabr.org/journal/article/a-manifesto-for-defensive-baseball-statistics/ Dial, C. (2009). Measuring defense: Entering the zones of fielding statistics. Baseball Research Journal, 38(1). https://sabr.org/journal/article/measuring-defense-entering-the-zones-of-fielding-statistics/ Franke, K. (2023). SMT data challenge 2023. GitHub. https://github.com/kaifranke/SMT-Data-Challenge-2023 Lindholm, S. (2017, November 7). The state of defensive sabermetrics: An overview. MSABR. https://msabr.com/2017/11/07/the-state-of-defensive-sabermetrics-an-overview/ Major League Baseball. (2024). Baseball savant. From https://baseballsavant.mlb.com/ Major League Baseball. (2024). Fielding run value. MLB.com. From https://www.mlb.com/glossary/statcast/fielding-run-value Major League Baseball. (2024). Outfielder jump. MLB.com. From https://www.mlb.com/glossary/statcast/outfielder-jump Major League Baseball. (2024). Statcast leaderboard. Baseball Savant. From https://baseballsavant.mlb.com/statcast_leaderboard PitchingBot. (2024). Using player & ball motion data to build a highly flexible defensive positioning algorithm in baseball. GitHub. https://github.com/PitchingBot/SMTDataChallenge # Appendix ## Code Breakdown Step 1: The Funnel (01_the_funnel.r): This script ingests raw, high-frequency player and ball tracking data. It iterates through every defensive play for a player, applies the five-component equations, and distills chaotic in-game events into clean, raw metrics. It produces two outputs: a play-by-play log for deep dive analysis and a summary file of each player's average raw scores to feed the next stage. Step 2: The Wrangler (02_intermediate_wrangling.r): Raw scores are meaningless without context. This script establishes statistical norms by calculating the mean for each metric at three hierarchical levels: league-wide, for general position groups (Outfield, Middle Infield, Corner Infield), and for specific primary positions (e.g., SS). This critical step generates a series of norm files that act as the statistical rulebook for the final scoring engine. Step 3: The Factory (03_the_factory.r): This final script is the synthesis engine where data and context are fused into narratives. First, it normalises each player's average raw scores against the appropriate positional benchmarks and scaling factors, converting each metric to the traditional 20-80 scouting scale, where ten points are equal to one standard deviation from the league mean.. These five component scores are then weighted based on positional demands, for example,  recognising that Path Efficiency is more critical for a centre fielder than a first baseman, to calculate the final Position-Adjusted CCS.  With the calculated scores, it performs archetype generation, analysing a player’s unique pattern of cognitive strengths and weaknesses to assign a multi-layered identity from the Cognitive Archetype Framework --- ## Methodological Calculations for Cognitive Metrics These tables detail the computational framework used to derive the five core cognitive metrics and their underlying physical models from raw player and ball tracking data. Each metric is designed to isolate a specific aspect of a defensive player's performance. #### Table 1: Core Cognitive Metric Calculations | | | | | |---|---|---|---| |Metric|Core Concept|Methodological Calculation|Output| |Reaction Time (RT)|Measures the latency between the critical stimulus (ball hit into play) and the player's first significant physical response.|The calculation begins with the player's 2D speed and the stimulus timestamp (t_stimulus). After a 100ms buffer, the algorithm identifies the first timestamp (t_first_move) where speed exceeds 1.37 m/s. The final RT is calculated as (t_first_move-t_stimulus).|A lower value indicates a quicker reaction and superior processing speed.| |Path Efficiency (PE)|Quantifies how direct a player's route was from their first move to the point of ball acquisition.|Player kinematic data from t_first_move to t_action_complete is analysed. The optimal path (d_optimal) is the straight-line Euclidean distance from the player's start to the fielding location. The actual path (d_actual) is the cumulative sum of all displacement segments travelled. The final ratio is Optimal Path / Actual Path, with the result capped at 1.0. <br> <br>Expanding this with MD could be done in the future with SMT's Route Acumen statistic|A score closer to 1.0 represents a perfectly direct route. Lower scores indicate path deviation and inefficiency.| |Movement Decisiveness (MD)|Assesses the fluidity of a player's movement, penalising periods of hesitation or sharp, inefficient changes in speed.|Player kinematics are analysed to identify hesitation events, defined as frames with sharp deceleration (acceleration_2d < -13.0 ft/s²) or sustained low speed (below 40% of the segment's maximum). The model calculates a hesitation density (ρ_hesitation).  The score is calculated via the formula: MD ≈ 1 / (1 + 1.5 * ρ_hesitation).|A score closer to 1.0 indicates a decisive, fluid movement pattern. Lower scores signify hesitation and inefficient flow.| |Adaptability (AD)|Measures a player's ability to quickly adjust their motor plan in response to a significant, unexpected deviation in the ball's flight path.|A predicted trajectory is compared to the actual ball path to find the first timestamp (t_disruption) where 3D deviation exceeds 5 feet. The player's post-disruption kinematics are then analysed to find the first adaptive movement (t_adapt), defined as a significant change in acceleration or speed. The metric is calculated as (t_adapt-t_disruption).|A lower value represents a faster and more effective adjustment to an unexpected event.| |Anticipation (AN)|Evaluates a player's pre-play positioning relative to a position-specific optimal starting location.|The player's median position in the 500ms before t_stimulus is identified to establish their starting location. Separately, the ball’s landing spot is determined by finding the coordinates at the lowest point of its post-contact trajectory. The distance between the player's start and the ball's landing spot (d_start→land) is then calculated and normalised into the final 0-1 score via the formula: AN ≈ 1 - (d_start→land / d_max).|A score closer to 1.0 indicates that the player was optimally positioned, demonstrating superior pre-play anticipation.| #### Table 2: Underlying Physical and Kinematic Models | | | | | |---|---|---|---| |Model|Core Concept|Methodological Calculation|Application| |Player Kinematics|To derive primary motion variables (velocity, acceleration) from raw time-series positional data.|Motion variables are derived using finite differences. Velocity (v) is the change in position over the change in time. Acceleration (a) is the change in velocity over the change in time. Two-dimensional speed and acceleration magnitudes are calculated via the Pythagorean theorem (speed_2d = sqrt(vx² + vy²)).|This is the foundational calculation for all five cognitive metrics, as they each rely on evaluating player speed or acceleration at critical moments.| |Ball Trajectory Prediction|To generate an expected, simplified flight path for the ball, serving as a baseline to detect deviations and player positioning|A ballistic projectile model is initiated using the ball's velocity vectors (vx, vy, vz) after contact. The model iteratively calculates subsequent positions at small time steps (dt = 0.04s), applying a constant downward acceleration for gravity (g = 32.174 ft/s²). Aerodynamic drag is considered negligible.|This model is the core engine for the Adaptability (AD) metric, as it provides the expected path against which the actual path is compared. It is also used to help calculate the Ball Landing Spot| |Ball Landing Spot Determination|To identify the precise destination coordinates of the batted ball for each specific play.|The model filters the ball's post-stimulus trajectory data and selects the coordinates of the single frame with the minimum vertical position (ball_position_z). This method identifies the landing or fielding point for both ground balls and fly balls.|This dynamically determined coordinate serves as the optimal target location for the play-specific Anticipation (AN) metric.| ## Positional Weights To calculate a player's final overall score, the framework applies unique weights to each cognitive skill, reflecting the specific mental and physical demands of their position group: | | | | | | | |---|---|---|---|---|---| |Position Group|Reaction Time (RT)|Path Efficiency (PE)|Decision-Making (MD)|Adaptability (AD)|Anticipation (AN)| |Outfield|20%|25%|15%|15%|25%| |Middle IF|25%|15%|25%|20%|15%| |Corner IF|25%|15%|20%|20%|20%| |Battery|30%|10%|15%|20%|25%| |General/Unknown|20%|20%|20%|20%|20%| These percentages are not arbitrary; they are designed to emphasise the most critical cognitive tools for each role in the field. However, they could be further refined by outside scouting and coaching advice. For Outfielders, success is defined by covering vast amounts of ground and getting a great jump on the ball. This is why Path Efficiency (25%) to take the best route and Anticipation (25%) to read the ball off the bat are weighted most heavily. For Middle Infielders (SS, 2B), the game unfolds in the most fast-paced and congested area of the field. The heavy emphasis on Reaction Time (25%) and Decision-Making (25%) reflects the need to process the game in a split second, make the correct read, and initiate complex actions like double plays without hesitation. For Corner Infielders (1B, 3B), players face a unique blend of scenarios, from the lightning-fast demands of the hot corner at third base to the crucial receiving plays at first. This is captured in a balanced profile that prioritises Reaction Time (25%) for handling hard-hit balls and errant throws, while also valuing a solid mix of Decision-Making, Adaptability, and Anticipation (20% each). Although not used in the final submission version of the CCS Framework: For the Battery (Catcher, Pitcher), the game is defined by a series of rapid, critical events. Blocking pitches in the dirt, reacting to bunts, and making snap throws are all dominated by pure reflexes, making Reaction Time (30%) their single most important cognitive skill, heavily supported by Anticipation (25%). For an Unknown or General position, this profile is used as a fallback when a player doesn't have enough data at a single position to be definitively categorised. To provide a fair baseline assessment, the Unknown/General profile applies an equal 20% weight to every cognitive skill. ## Statistical Normalisation | | | | |---|---|---| |Parameter|Value|Purpose| |Positional Weights|Differs between Positional Groups. See the above section|These weights are applied to the five component scores to create a score that reflects the unique cognitive demands of a player's role.| |Mean|50|The scale is centred so that the league-wide average is 50 for all metrics.| |Standard Deviation|10|This is fixed for both the metrics and the overarching CCS. Every 10 points away from 50 represents exactly one standard deviation from the league average. A positional adjusted CCS score of 60 means the player is in the top ~16% of the league; a 70 puts them in the top ~2.5%.| |Min / Max Clamp|20 / 80|Enforces the traditional scouting scale. A calculation that results in a score of 85 will be capped at 80; a score of 15 will be capped at 20. A player will only receive an 80 if they are a true outlier, performing at 3 or more standard deviations above the league average.| ##  --- ## Archetypes ### Layer 1: Foundational Profile The foundational layer provides a quick-look grade of a player's overall cognitive performance, assigning one tag based on their final score modelled based off percentages and other splits on the 20-80 Scale.. | | | | |---|---|---| |Archetype|Scouting Description|Trigger Formula| |Cognitive Superstar|An elite, all-around cognitive performer who consistently processes the game at the highest level.|Position_Adjusted_CCS > 70| |Strong Cognitive Tools|An above-average cognitive player whose mental processing is a clear asset on the field.|60 <= Position_Adjusted_CCS < 70| |Average Profile|A player with solid, league-average cognitive skills. Not a liability, but lacks a standout tool.|40 <= Position_Adjusted_CCS < 60| |Developing Cognitive Tools|A below-average cognitive player who shows inconsistencies and has clear areas for development.|35 <= Position_Adjusted_CCS < 40| |Cognitive Prospect|A raw player whose cognitive skills are a significant weakness that requires intensive coaching.|Position_Adjusted_CCS < 35| ### Layer 2: Style Profile The 2nd layer describes a player's primary approach or style, identifying their go-to method for winning matchups or the core pattern of their limitations. | | | | |---|---|---| |Archetype|Scouting Description|Trigger Formula| |Erratic Performer|An inconsistent and unpredictable player. Flashes moments of brilliance and moments of total confusion.|Standard Deviation of the five component scores > 16.0 AND Overall CCS < 60| |Complete Processor|A dual-threat who wins with both pre-play intelligence and in-play reflexes. Can beat you in multiple ways.|Has a score >= 55 in a "Thinker" skill (AN/MD) AND a score >= 55 in a "Reactor" skill (RT/AD).| |Technician|Wins with flawless mechanics. Their defining trait is economical and repeatable movement.|Path Efficiency (PE) is their #1 ranked component score.| |Thinker|Wins with the mind. Their primary approach is to out think the opposition before the play develops.|Anticipation (AN) or Decision-Making (MD) are one of their top two component scores.| |Reactor|Wins with instincts. Their primary approach is to out-maneuver the opposition with sharp reflexes.|Reaction Time (RT) or Adaptability (AD) are one of their top two component scores.| |Labored Mover|A player whose physical read-and-react tools or inefficient movement are their primary limitation.|Has no skill scores >= 55 AND their single weakest skill is either Reaction Time (RT) or Path Efficiency (PE).| |Hesitant Processor|A player whose primary limitation is being slow to read the game or commit to an action.|Has no skill scores >= 55 AND their single weakest skill is either Anticipation (AN) or Decision-Making (MD).| |Jack of All Trades|Master of none. While not a liability, they lack a specific cognitive weapon to consistently win matchups.|Player has no "Clear Asset," "Area of Concern," or complex Trade-Off tags.| ### Layer 3: Complex Trade-Offs The narrative layer highlights specific, high-leverage skill combinations that tell a deeper story about a player's unique strengths and weaknesses. | | | | |---|---|---| |Archetype|Scouting Narrative|Trigger Formula| |Aggressive Gambler|Sees the play developing early but often over-commits or chooses the wrong response. High-risk, high-reward.|AN %ile > 75% AND MD %ile < 40%| |Explosive but Inefficient|An elite first-step reactor whose burst is often wasted by taking poor angles or routes.|RT %ile > 75% AND PE %ile < 40%| |Delayed Reactor|Knows where the play is going but lacks the athletic burst to get there on time. The mind is willing, but the feet are late.|AN %ile > 75% AND RT %ile < 40%| |Fluid Mover, Flawed Decisions|Incredible Mover,  but freezes, hesitates, or makes a poor choice at the key moment.|PE %ile > 75% AND MD %ile < 40%| |Strategic but Inefficient|A smart player who correctly identifies the play but takes such poor routes that they are often late to the spot.|(AN %ile > 75% OR MD %ile > 75%) AND PE %ile < 40%| |System Dependent|Thrives when the play is structured and their role is clear, but struggles to adapt when things break down into chaos.|MD %ile > 75% AND AD %ile < 40%| |Chaotic Creator|A natural playmaker who excels in broken plays but can be prone to poor decisions in a structured system.|AD %ile > 75% AND MD %ile < 40%| |Slow to Ignite|Lacks an initial burst off the mark, but once in motion, moves with impressive economy.|PE %ile > 75% AND RT %ile < 40%| |One Step Behind|Constantly a step behind the initial action because they can't read the play, but are brilliant at recovering and adapting.|AD %ile > 75% AND AN %ile < 40%| ### Layer 4: Individual Qualities The final layer adds specific, granular tags for any individual skills that are standout strengths or notable weaknesses. In the web portal, a colour scale and icon system is used to mark the qualities, as well as best/worst asset. | | | | |---|---|---| |Archetype|Scouting Description|Trigger Formula| |Dominant [Skill]|A game-changing, elite-level tool that defines their play.|Percentile Rank > 95%| |Clear Asset in [Skill]|A reliable strength and a clear positive for their game.|75% < Percentile Rank <= 95%| |Area of Concern in [Skill]|An inconsistent skill that creates a noticeable weakness in their profile.|5% <= Percentile Rank < 25%| |Significant Flaw in [Skill]|A major weakness that will likely limit their ceiling and requires intensive coaching.|Percentile Rank < 5%|