Chapter 37 Exercises: Discipline, Systems, and Record-Keeping

Instructions: Complete all exercises in the parts assigned by your instructor. Written responses should demonstrate clear reasoning with concrete examples. Programming exercises require type hints, docstrings, and working sample output. System design exercises should include specific, actionable details rather than vague aspirations.

Part A: Betting Journal Design

Each problem is worth 5 points. Answer in complete sentences.

Exercise A.1 --- Journal Field Prioritization

You are designing a minimal viable betting journal for a bettor who currently keeps no records. They have told you they will abandon any system requiring more than 90 seconds per bet. Identify the eight most critical fields (from the comprehensive list in Section 37.1.2) that must be captured per bet, rank them by importance, and justify your ranking. Explain what analytical capabilities are lost by omitting the remaining fields.

Exercise A.2 --- Digital vs. Physical Trade-offs

A bettor uses a leather notebook for pre-bet reasoning and emotional state notes, and a Google Sheet for quantitative data. Describe three specific risks of this dual-system approach, propose a workflow that mitigates each risk, and explain why data integrity is more important than data completeness for performance analysis.

Exercise A.3 --- Journal Schema Design

Design a relational database schema (tables, columns, data types, and relationships) for a betting journal that supports the full field list from Section 37.1.2. Your schema must handle: multiple sportsbook accounts, parlays and same-game parlays (with linked legs), live bets referencing a pre-game parent bet, and emotional state tracking. Provide the SQL CREATE TABLE statements for at least three core tables.

Exercise A.4 --- Journal Discipline Failure Modes

List five specific ways a bettor's journal discipline can break down over time (beyond simply forgetting to log bets). For each failure mode, describe the psychological mechanism driving it, the analytical consequences, and a system design that makes the failure mode less likely.

Exercise A.5 --- Retroactive Journal Construction

A bettor has six months of sportsbook transaction history (deposits, withdrawals, individual bet settlements) but no journal. Describe a methodology for constructing a partial retrospective journal from this data. What fields can be recovered, what fields are permanently lost, and what analytical limitations does the reconstructed journal have compared to a contemporaneous one?

Exercise A.6 --- Privacy and Security

Your betting journal contains detailed financial data, sportsbook account information, and personal emotional state records. Design a data security protocol covering: storage encryption, backup strategy, access controls, and data retention policies. Explain why each component matters specifically for betting journal data as opposed to generic personal data.

Part B: Performance Analysis and Review

Each problem is worth 5 points.

Exercise B.1 --- Weekly Review Template

Design a complete weekly review template that a bettor would fill out every Sunday evening. The template should include: quantitative metrics (with formulas), process adherence checks, emotional review questions, and specific action items for the following week. The entire review should be completable in 45 minutes or less.

Exercise B.2 --- CLV Analysis Interpretation

A bettor's monthly review shows the following: 200 bets, 52.8% win rate, -1.3% ROI, but +2.1 cents average CLV per dollar wagered. Explain this apparently contradictory result. Is this bettor likely to be profitable long-term? What specific advice would you give based on these numbers? How many additional bets are needed before the ROI becomes a reliable signal?

Exercise B.3 --- Category Breakdown Analysis

A bettor discovers the following in their monthly review: NFL sides +8.2% ROI (80 bets), NBA sides -3.1% ROI (120 bets), MLB moneylines +1.4% ROI (90 bets), NFL totals -6.8% ROI (40 bets). Calculate the blended ROI, identify which category should be expanded and which should be reduced or eliminated, and explain why naively eliminating all negative-ROI categories could be a mistake at these sample sizes.

Exercise B.4 --- Drawdown Analysis

Write a mathematical framework for expected maximum drawdown as a function of edge, win rate, odds, and sample size. Using this framework, calculate the probability that a bettor with a 3% edge on -110 bets (55.3% win rate) experiences a drawdown of 20% or more during a 1,000-bet sample with flat 2% of bankroll staking. Discuss how this calculation should inform the bettor's psychological preparation.

Exercise B.5 --- Sharpe Ratio Adaptation

The Sharpe ratio in finance is defined as (mean return - risk-free rate) / standard deviation of returns. Adapt this metric for sports betting contexts. Define the appropriate measurement period (per bet, per day, per week), explain what constitutes the "risk-free rate" in betting, discuss the limitations of applying financial metrics to betting, and calculate the betting Sharpe ratio for a bettor with 2.5% yield and 15% standard deviation of daily returns.

Exercise B.6 --- Automated Alert System

Design a system of automated alerts that would be triggered during the monthly review. Specify at least eight alert conditions (e.g., "CLV has been negative for 3 consecutive weeks"), the threshold for each, and the recommended action when triggered. Distinguish between informational alerts, warning alerts, and critical alerts.

Exercise B.7 --- Benchmark Construction

A bettor needs an objective benchmark against which to measure their performance. Describe three possible benchmarks for a bettor specializing in NFL sides: one based on closing line performance, one based on public consensus, and one based on a naive model. Explain the strengths and weaknesses of each and recommend which should be used as the primary benchmark.

Part C: System Design and Implementation

Each problem is worth 10 points. These require both written analysis and code.

Exercise C.1 --- Pre-Bet Checklist Engine

Write a Python class that implements an interactive pre-bet checklist. The class should: (a) present each checklist item sequentially and record yes/no responses, (b) compute a composite qualification score, (c) return a go/no-go recommendation based on a configurable threshold, (d) log the checklist results alongside the bet in the journal, and (e) track checklist compliance rates over time. Include at least 10 checklist items drawn from Section 37.3.1.

Exercise C.2 --- Performance Dashboard Extension

Extend the PerformanceDashboard class from Section 37.2.4 to include: (a) a time-of-day analysis showing performance by hour of bet placement, (b) a day-of-week analysis, (c) a correlation matrix between all tracked metrics, and (d) a "what-if" scenario tool that estimates how total P&L would change if specific categories of bets were eliminated. Write the code and demonstrate with synthetic data.

Exercise C.3 --- A/B Testing Framework

Implement a Python class for A/B testing model variations in a betting context. The class should: (a) accept two sets of model predictions and corresponding outcomes, (b) compute CLV and ROI for each model, (c) perform a paired statistical test to determine if one model significantly outperforms the other, (d) compute the required sample size for a given minimum detectable effect and power, and (e) produce a visual comparison. Apply the framework to synthetic data demonstrating a model with and without a new feature.

Exercise C.4 --- Discipline Adherence Tracker

Write a Python program that monitors a bettor's discipline adherence over time. It should: (a) track each bet against the documented rules (stake limits, edge thresholds, time restrictions, volume caps), (b) compute daily, weekly, and monthly adherence rates, (c) identify patterns in rule violations (time of day, sport, emotional state), (d) generate a "discipline score" from 0-100, and (e) produce a trend chart showing adherence over time. The system should read from the journal format defined in Section 37.1.4.

Exercise C.5 --- Five Whys Automation

Design and implement a structured "Five Whys" analysis tool for betting mistakes. The tool should: (a) prompt the user through five levels of causal analysis, (b) categorize root causes into predefined categories (emotional, analytical, process, environmental), (c) suggest remediation actions based on the root cause category, (d) store completed analyses in a searchable database, and (e) detect recurring root causes across multiple analyses. Include example output for a scenario where a bettor placed an oversized bet after a losing streak.

Part D: Process Documentation and Improvement

Each problem is worth 7 points.

Exercise D.1 --- Operating Manual Draft

Write a complete three-page operating manual for a hypothetical bettor specializing in NBA spreads. The manual should cover: model description (inputs, outputs, edge threshold), staking rules (including Kelly fraction and caps), qualification criteria, pre-bet checklist, review schedule, stopping rules, and exception process. The manual should be specific enough that another person could execute the strategy.

Exercise D.2 --- Kaizen Implementation Plan

Design a 12-week Kaizen implementation plan for a bettor transitioning from informal to systematic betting. Each week should have a specific focus area, a measurable improvement target, and a defined metric for evaluating success. The plan should be incremental, with each week building on the previous one. Explain why attempting to implement all systems simultaneously is counterproductive.

Exercise D.3 --- Feedback Loop Analysis

Draw a complete feedback loop diagram for a betting operation, showing how data flows from bet placement through outcome recording, performance analysis, process modification, and back to bet placement. Identify at least three points where the feedback loop can break or introduce bias. For each break point, describe the consequence and a repair mechanism.

Exercise D.4 --- Post-Mortem Template

Design a post-mortem template for significant discipline violations (losses exceeding 5% of bankroll in a single day, or more than two process deviations in a session). The template should include: factual event description, quantified financial impact, Five Whys root cause analysis, specific corrective actions, timeline for implementation, and follow-up verification. Demonstrate the template with a worked example.

Exercise D.5 --- Compounding Improvement Calculator

Write a Python function that computes the long-term impact of small process improvements on betting profitability. The function should model how a weekly improvement of X% in either edge identification or execution discipline compounds over a season, a year, and three years. Visualize the results and discuss why this calculation, while simplified, captures an important truth about the returns to systematic improvement.

Part E: Synthesis and Application

Each problem is worth 5 points.

Exercise E.1 --- System Integration

Describe how the five systems from this chapter (journal, performance analysis, systematic process, automated discipline, continuous improvement) integrate into a single daily workflow for a bettor who places 5-8 bets per day. Provide a minute-by-minute timeline for a typical betting day, from pre-session preparation through post-session review.

Exercise E.2 --- System Failure Modes

Identify five ways the discipline systems described in this chapter could fail or produce counterproductive results (e.g., over-optimization of past data, analysis paralysis, false sense of security). For each failure mode, explain the mechanism and propose a safeguard.

Exercise E.3 --- Cross-Chapter Integration

Write an essay (400-600 words) explaining how the systems in Chapter 37 address the specific psychological vulnerabilities identified in Chapter 36. Map each of the six major cognitive biases to the specific system component(s) designed to counteract it. Discuss whether systems can fully eliminate bias or merely reduce it.

Exercise E.4 --- Scalability Analysis

A bettor currently placing 5 bets per week wants to scale to 30 bets per week. Analyze how each of the five systems must adapt to handle the increased volume. Which systems scale linearly, which require step-function investments, and which become more effective at higher volume? What is the minimum infrastructure a bettor needs before scaling volume?

Exercise E.5 --- Teaching System Design

You are mentoring a new bettor who has read Chapters 36 and 37 but has placed only 50 lifetime bets. Design a progressive onboarding plan that introduces the five systems over a 60-day period. For each system, specify when it is introduced, what minimum viable version is implemented first, and what metrics indicate readiness to add the next system.