Chapter 10: Exercises
Exercise Overview
These exercises will help you develop practical skills in special teams analytics. Work through each level sequentially, as concepts build upon previous exercises.
Level 1: Conceptual Understanding
Exercise 1.1: Field Goal Decision Components
Consider a 4th and 3 situation at the opponent's 28-yard line with 8 minutes remaining in the 3rd quarter (score tied).
Questions: a) What is the approximate field goal distance from this position? b) List three factors that would affect the probability of making this field goal. c) What information would you need to calculate the expected points for attempting the field goal versus going for it? d) Why might weather conditions affect field goal probability more at longer distances?
Exercise 1.2: Punt Value Components
A punter averages 44 yards per punt with a 4.2-second hang time.
Questions: a) Why is hang time important for evaluating punt quality? b) What is "net punting" and why is it more useful than gross punting average? c) How does field position affect the optimal punt strategy? d) What makes a "coffin corner" punt valuable, and when should teams attempt them?
Exercise 1.3: Kickoff Strategy Evolution
Modern college football has seen significant changes in kickoff rules and strategies.
Questions: a) Explain the tradeoff between kicking deep versus directional kicking. b) Why has the touchback rate increased significantly in recent years? c) What is an "expected starting position" and how does it help evaluate kickoff units? d) How do you evaluate a kickoff that results in a touchback versus one returned to the 22-yard line?
Exercise 1.4: Coverage Unit Evaluation
A punt coverage unit allows an average of 8.2 yards per return.
Questions: a) Is 8.2 yards per return good, average, or poor? What benchmarks would you use? b) What individual metrics would help identify the best gunners on the team? c) How does return average interact with punting distance to determine net value? d) Why might a team with poor coverage allow fewer return yards than a team with good coverage?
Exercise 1.5: Fourth Down Decision Framework
A coach faces 4th and 2 at the opponent's 35-yard line in the 2nd quarter.
Questions: a) What are the three main options, and what is the expected field goal distance? b) For the "go for it" option, what conversion probability would you estimate for 4th and 2? c) How would this decision change if the team were down by 14 points versus tied? d) What game context factors should influence this decision beyond pure expected points?
Level 2: Basic Calculations
Exercise 2.1: Field Goal Probability Model
Build a simple field goal probability model using historical data.
Given data: | Distance | Attempts | Makes | Make % | |----------|----------|-------|--------| | 20-24 | 45 | 43 | 95.6% | | 25-29 | 62 | 58 | 93.5% | | 30-34 | 78 | 68 | 87.2% | | 35-39 | 85 | 69 | 81.2% | | 40-44 | 72 | 51 | 70.8% | | 45-49 | 58 | 34 | 58.6% | | 50+ | 42 | 18 | 42.9% |
Tasks: a) Calculate the weighted average make percentage for all attempts. b) Using linear regression, find the equation relating distance to make percentage. c) Predict the make percentage for a 47-yard field goal. d) At what distance does the model predict a 50% make rate?
Exercise 2.2: Punting Net Value
Calculate comprehensive punting metrics.
Punter Statistics (Season): - Total punts: 58 - Gross punting yards: 2,552 - Punts inside 20: 22 - Touchbacks: 6 - Fair catches: 28 - Return yards allowed: 186 - Punts blocked: 1
Tasks: a) Calculate gross punting average. b) Calculate net punting average. c) Calculate the inside-20 percentage. d) Calculate touchback percentage. e) What is the return average on punts that were returned?
Exercise 2.3: Kickoff Expected Starting Position
Evaluate kickoff performance using expected starting position.
Kickoff Data: | Result Type | Count | Avg Start Position | |------------------|-------|-------------------| | Touchback | 42 | 25-yard line | | Returned | 35 | 24-yard line | | Out of bounds | 3 | 35-yard line | | Onside attempt | 2 | Own 45 (recovered 1) |
Tasks: a) Calculate the overall expected starting position for opponents. b) Compare the value of touchbacks versus returns in this data. c) Calculate the kickoff coverage efficiency (return yards allowed per return opportunity). d) What is the cost in field position of the out-of-bounds kicks?
Exercise 2.4: Fourth Down Expected Points
Calculate expected points for a fourth-down decision.
Situation: 4th and 3 at opponent's 32-yard line
Given: - Conversion probability for 4th and 3: 52% - Field goal distance: 49 yards - Field goal make probability (49 yards): 55% - Expected punt net yards from this position: 35 yards - EP at opponent's 32: 2.8 - EP at own 25 (after failed conversion): -0.8 - EP after made field goal: 3.0 - EP after missed field goal (opponent's 39): 1.2 - EP at opponent's 3 (after punt): -1.8
Tasks: a) Calculate expected points for going for it. b) Calculate expected points for attempting the field goal. c) Calculate expected points for punting. d) What is the optimal decision? By how much?
Exercise 2.5: Special Teams Play Value
Calculate the value added by special teams plays.
Play-by-Play Data: | Play | Type | Result | Yard Line Before | Yard Line After | |------|------|--------|-----------------|-----------------| | 1 | Punt | 45 yd, fair catch | Own 22 | Opp 33 | | 2 | FG | Made, 38 yd | Opp 20 | - | | 3 | Kickoff | TB | - | Opp 25 | | 4 | Punt | 52 yd, returned 12 | Own 15 | Opp 45 | | 5 | FG | Missed, 47 yd | Opp 29 | Opp 37 |
Using EP estimates: Own 22: -0.2, Opp 33: 1.8, Opp 25: 1.2, Own 15: -0.8, Opp 45: 2.4, Opp 37: 2.0
Tasks: a) Calculate the expected points added (EPA) for each punt. b) Calculate the field position value change for the kickoff. c) Calculate the EPA for the missed field goal. d) What was the total special teams EPA for these five plays?
Level 3: Implementation Challenges
Exercise 3.1: Complete Field Goal Model
Build a comprehensive field goal probability model.
"""
Create a FieldGoalProbabilityModel class that:
1. Calculates baseline probability from distance
2. Adjusts for weather (wind speed, precipitation, temperature)
3. Adjusts for surface type (grass vs turf)
4. Adjusts for game situation (pressure)
5. Includes kicker-specific adjustment based on historical performance
Required methods:
- predict_probability(distance, weather_conditions, surface, kicker_stats)
- evaluate_kicker(kicker_attempts) -> Dict with accuracy by range
- calculate_expected_points(distance, conditions) -> float
"""
Test your model: - 35-yard FG, clear weather, grass: Should be ~82% - 50-yard FG, 15 mph wind, rain: Should be ~35-45% - 42-yard FG, neutral conditions, elite kicker: Should be ~78%
Exercise 3.2: Punting Analytics System
Implement a complete punting evaluation system.
"""
Create classes to analyze all aspects of punting:
1. PuntAnalyzer:
- calculate_gross_average(punts)
- calculate_net_average(punts)
- calculate_inside_20_rate(punts)
- evaluate_hang_time(punts)
- calculate_punt_value(punt) -> EPA for single punt
2. PunterComparator:
- compare_punters(punter_a_stats, punter_b_stats)
- rank_by_composite_score(punters_list)
3. PuntSituationAnalyzer:
- optimal_strategy(field_position, score_differential, time_remaining)
- should_punt(situation_dict) -> bool with reasoning
"""
Sample punter data for testing:
punter_a = {
'punts': 55, 'gross_yards': 2420, 'inside_20': 20,
'touchbacks': 5, 'returns': 22, 'return_yards': 154,
'avg_hang_time': 4.3, 'fair_catches': 23
}
punter_b = {
'punts': 48, 'gross_yards': 2232, 'inside_20': 24,
'touchbacks': 2, 'returns': 18, 'return_yards': 108,
'avg_hang_time': 4.5, 'fair_catches': 26
}
Exercise 3.3: Kickoff Optimization Model
Build a model to optimize kickoff strategy.
"""
Create a KickoffStrategyOptimizer class that:
1. Calculates expected starting position for different kick types:
- Deep kick (touchback potential)
- Directional kick (corner)
- Sky kick (high, short)
- Onside kick
2. Evaluates coverage unit performance
3. Recommends optimal strategy based on:
- Kicker leg strength
- Coverage unit quality
- Return threat level
- Game situation
Methods:
- calculate_expected_position(kick_type, kicker_stats, coverage_rating)
- recommend_strategy(situation_dict) -> str with explanation
- evaluate_kick_result(kick_data) -> float (EPA)
"""
Exercise 3.4: Fourth Down Decision Engine
Build a comprehensive fourth-down decision model.
"""
Create a FourthDownDecisionEngine that:
1. Calculates expected points for all three options
2. Incorporates game state (score, time, possession context)
3. Accounts for team-specific factors (conversion rate, FG accuracy, punting)
4. Provides clear recommendation with confidence level
Required methods:
- analyze(situation_dict) -> Dict with all options and recommendation
- conversion_probability(distance, yard_line) -> float
- field_goal_expected_points(distance, kicker_stats) -> float
- punt_expected_points(field_position, punter_stats) -> float
- get_recommendation(analysis) -> str with explanation
Include adjustments for:
- Score differential
- Time remaining
- Home/away
- Conference play context
"""
Exercise 3.5: Special Teams Rating System
Create a comprehensive special teams rating system.
"""
Build a SpecialTeamsRatingSystem that:
1. Evaluates each phase:
- Field goals (attempts, accuracy by range, clutch kicks)
- Punting (gross, net, inside 20, hang time)
- Kickoffs (expected starting position, touchback rate)
- Punt returns (average, efficiency)
- Kick returns (average, starting position gained)
- Coverage units (yards allowed per return)
2. Calculates composite rating
3. Compares to league averages
4. Identifies strengths and weaknesses
5. Estimates points above average from special teams
Output: Team special teams report card with grades and rankings
"""
Level 4: Advanced Analysis
Exercise 4.1: Weather Impact Analysis
Analyze how weather affects special teams performance.
Tasks: a) Using provided historical data, quantify the impact of: - Wind speed on field goal accuracy (by distance) - Temperature on punting distance - Precipitation on return performance
b) Build adjustment factors for your models.
c) Create visualizations showing: - FG accuracy vs wind speed - Punt distance vs temperature - Return average vs weather conditions
d) Calculate the expected points value of having a dome stadium for special teams.
Exercise 4.2: Fourth Down Decision Audit
Analyze a team's fourth-down decisions over a season.
Tasks: a) Collect all fourth-down decisions from a season (go/punt/FG).
b) For each decision, calculate: - Expected points for each option - Optimal decision - Decision quality (actual vs optimal) - Points lost from suboptimal decisions
c) Create a report showing: - Total decisions made - Optimal decision rate - Estimated points lost to conservative decisions - Estimated points lost to aggressive decisions - Situational tendencies (by field position, score, time)
d) Identify the single most costly decision of the season.
Exercise 4.3: Return Specialist Evaluation
Develop a comprehensive return specialist rating.
Tasks: a) Define the components of return value: - Starting position (touchback alternative) - Average return yards - Return rate (returns/opportunities) - Explosive returns (20+ yards) - Fumbles/muffs
b) Build a model that calculates expected return yards based on: - Starting position - Kick/punt quality - Number of coverage players in area
c) Calculate "Return Yards Over Expected" for each return.
d) Create a composite rating that accounts for volume and efficiency.
e) Apply your model to compare two returners with different profiles: - Returner A: High volume, steady returns, no explosives - Returner B: Selective returns, boom-or-bust, occasional muff
Exercise 4.4: Game-Winning Probability Impact
Analyze how special teams affects win probability.
Tasks: a) Calculate the win probability impact of: - A made 45-yard field goal vs a miss - A punt inside the 10 vs a touchback - A kickoff return for touchdown - A blocked punt
b) Build a model that tracks cumulative special teams WPA per game.
c) Analyze which special teams plays have the highest variance in WPA.
d) Calculate the correlation between special teams WPA and game outcomes.
e) Identify the "clutch" situations where special teams matters most.
Exercise 4.5: Scheme and Personnel Optimization
Optimize special teams unit composition.
Tasks: a) Given roster constraints, determine optimal personnel for: - Punt coverage unit (balance speed vs tackling) - Field goal protection (blocking vs emergency coverage) - Kickoff coverage (lanes and assignment)
b) Model the expected value of different: - Punt protection schemes (6-man vs 7-man) - Field goal formations (standard vs swinging gate option) - Kickoff coverage alignments
c) Calculate the break-even point for fake punts/FGs based on: - Conversion probability - Field position - Expected surprise factor
d) Recommend practice time allocation across special teams phases based on impact analysis.
Level 5: Research Projects
Exercise 5.1: Historical Trends in Special Teams
Research how special teams has evolved in college football.
Research Questions: a) How have touchback rates changed over the past 20 years? What rule changes drove this?
b) Analyze the trend in fourth-down aggressiveness across college football. Which conferences lead?
c) How has the value of elite punters changed as offenses have improved?
d) Compare special teams strategy between NFL and college. What differs and why?
Deliverables: - Data analysis with visualizations - Statistical trend analysis - Future projections - Recommendations for strategy adaptation
Exercise 5.2: Building a Live Decision Tool
Create a real-time fourth-down decision tool.
Requirements: a) Input interface for: - Field position, down, distance - Score differential and time remaining - Team-specific parameters - Weather conditions
b) Model that calculates: - Expected points for all options - Win probability impact - Confidence intervals
c) Output that provides: - Clear recommendation - Visualization of option comparison - Sensitivity analysis - Historical context
d) Validation against actual decisions and outcomes.
Exercise 5.3: Special Teams Value in Recruiting
Analyze the value of recruiting special teams specialists.
Research Questions: a) What is the career value of a scholarship kicker vs using walk-ons?
b) How do recruiting ratings predict special teams performance?
c) What is the optimal recruiting investment in specialists vs position players?
d) How does special teams performance correlate with team success at different program levels?
Analysis Components: - ROI calculation for scholarship specialists - Performance prediction models - Program-specific recommendations - Case studies of elite special teams programs
Exercise 5.4: Blocked Kick Analysis
Deep dive into blocked kicks and their prevention.
Research Questions: a) What factors predict blocked kicks? - Protection scheme - Snap-to-kick time - Rush patterns - Situational tendencies
b) Build a blocked kick probability model.
c) Calculate the expected value of: - Adding a protector - Trading distance for quicker operation - Using rugby-style punts
d) Create a scouting framework for identifying block opportunities.
Exercise 5.5: Conference Special Teams Analysis
Compare special teams performance and strategy across conferences.
Analysis Components: a) Calculate conference-level metrics for: - Field goal accuracy by range - Punting averages (gross and net) - Kickoff starting positions - Return averages - Fourth-down conversion rates
b) Identify conference-specific strategies and tendencies.
c) Analyze weather/climate impact by conference.
d) Model the special teams advantage in cross-conference games.
e) Create a conference special teams power ranking with methodology documentation.
Submission Guidelines
For all exercises: 1. Include all code with comments explaining your methodology 2. Provide sample output demonstrating your solution works 3. Include visualizations where appropriate 4. Document any assumptions you made 5. Discuss limitations of your approach
For research projects: 1. Submit a written report (2,000-3,000 words) 2. Include all supporting code and data 3. Create presentation-ready visualizations 4. Provide actionable recommendations 5. Discuss future research directions