> Source URL: /ai-and-sports/projects/6-predicting-the-future/predicting-the-future.football.project
---
title: Predicting the Future - Points, Performance & Breakouts (Football)
student_outcomes:
  - use dictionaries to store structured player and game data
  - find simple patterns in recent performance data
  - build a reusable prediction function for next-game output
  - label breakout candidates with a clear rule-based threshold
---

# Project: Predicting the Future - Points, Performance & Breakouts (Football)

---

## Introduction

Football prediction often starts with a simple baseline. If you know a quarterback's recent efficiency and expected volume, you can estimate next-game output.

In this project, you will build a lightweight prediction model and flag breakout-watch performances.

### What we're building

By the end of this project, you will:

- **Store player data in dictionaries**
- **Detect trends** in recent production
- **Predict next-game passing yards** from recent rates
- **Classify breakout potential** with clear rules

### Why this matters

If you can build and explain a simple baseline model, you are learning core analyst thinking.

---

## Part 1: Quick Start

### Set up your project in Cursor

For this project, you will create your files from scratch (no starter pack download).

#### Before you start: quick vocabulary

- A **folder** is a container that holds files.
- A **file** is one item inside a folder.
- A file **extension** is the ending in the file name:
  - `.py` means Python code.
  - `.csv` means table-style data.

1. Pick an easy location for your project folder:
   - Mac: `Desktop` in Finder
   - Windows: `Desktop` in File Explorer
   - Chromebook: `My files` in the Files app
2. Create a folder called `football-performance-predictor`.
3. In Cursor, go to `File > Open Folder` and open `football-performance-predictor`.
4. In Cursor's left file explorer:
   - click **New Folder** and create `data`
   - click **New File** and create `performance_predictor.py`
   - open `data`, click **New File**, and create `recent_games.csv`
5. If you do not see file extensions on your computer, that is okay. Type the full names exactly, including `.py` and `.csv`.

### Writing the code

Type this into `performance_predictor.py`:

```python
recent_passing_yards = [245, 271, 298, 260, 312]
recent_attempts = [34, 36, 39, 35, 40]
projected_attempts = 37

yards_per_attempt = sum(recent_passing_yards) / sum(recent_attempts)
predicted_passing_yards = yards_per_attempt * projected_attempts

print(f"Predicted passing yards next game: {predicted_passing_yards:.1f}")
```

You just built a simple model:
`prediction = yards per attempt x projected attempts`

### Running the code

Save the file (<Kbd>Cmd + S</Kbd> on Mac, <Kbd>Ctrl + S</Kbd> on Windows/Chromebook).

Run the file with Cursor's built-in **Run Python File** play button in the top-right.

If the play button is missing, install/enable the Python extension in Cursor and reopen `performance_predictor.py`.

Expected output:

```
Predicted passing yards next game: 281.4
```

If your output matches, your setup is working.

---

## Part 2: Project Milestones

### Milestone 1: Store player data in a dictionary

Dictionaries let you name each value so your code is easier to read.

Add this below your existing code:

```python
player = {
    "name": "Aiden Brooks",
    "team": "Greenville Knights",
    "season_average_yards": 254.7,
    "recent_passing_yards": [245, 271, 298, 260, 312],
    "recent_attempts": [34, 36, 39, 35, 40]
}

print(player["name"])
print(player["recent_passing_yards"])
```

Expected output:

```text
Aiden Brooks
[245, 271, 298, 260, 312]
```

---

### Milestone 2: Build reusable prediction functions

Now wrap your logic into reusable functions.
This makes your model easy to reuse for more players.

Add this below Milestone 1:

```python
def average(values):
    return sum(values) / len(values)

def predict_passing_yards(recent_passing_yards, recent_attempts, projected_attempts):
    yards_per_attempt = sum(recent_passing_yards) / sum(recent_attempts)
    return yards_per_attempt * projected_attempts

projected_attempts = 37
prediction = predict_passing_yards(
    player["recent_passing_yards"],
    player["recent_attempts"],
    projected_attempts
)

print(f"{player['name']} predicted passing yards: {prediction:.1f}")
```

Expected output:

```text
Aiden Brooks predicted passing yards: 281.4
```

---

### Milestone 3: Detect trend direction

Prediction is not just one number. Analysts also ask: "Is this player trending up or down?"

Add this below your current code:

```python
recent = player["recent_passing_yards"]
first_half_avg = average(recent[:2])
second_half_avg = average(recent[-2:])

if second_half_avg > first_half_avg:
    trend = "up"
elif second_half_avg < first_half_avg:
    trend = "down"
else:
    trend = "flat"

print(f"Trend: {trend}")
```

Expected output:

```text
Trend: up
```

---

### Milestone 4: Add breakout watch logic

Now convert prediction + season average into a label.

Add this below your current code:

```python
def breakout_label(predicted_value, season_average, breakout_margin):
    if predicted_value >= season_average + breakout_margin:
        return "Breakout watch"
    if predicted_value >= season_average:
        return "On pace"
    return "Below season pace"

tag = breakout_label(prediction, player["season_average_yards"], 30.0)
print(f"Tag: {tag}")
```

Expected output:

```text
Tag: On pace
```

---

### Milestone 5: Load recent game logs from CSV

Now keep game data in a CSV so you can update predictions without editing Python logic.

1. Open `data/recent_games.csv`.
2. Paste the data below.
3. Save the file.
4. Make sure the file name is exactly `recent_games.csv` (not `recent_games.csv.txt`).

Paste this into `data/recent_games.csv`:

```text
date,passing_yards,attempts
2026-02-01,245,34
2026-02-05,271,36
2026-02-09,298,39
2026-02-12,260,35
2026-02-15,312,40
```

Now load that CSV in Python and re-run the prediction from file data.
Add this below your existing code:

```python
import csv

recent_passing_yards = []
recent_attempts = []

with open("data/recent_games.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        recent_passing_yards.append(float(row["passing_yards"]))
        recent_attempts.append(float(row["attempts"]))

player["recent_passing_yards"] = recent_passing_yards
player["recent_attempts"] = recent_attempts
prediction = predict_passing_yards(recent_passing_yards, recent_attempts, projected_attempts)

first_half_avg = average(recent_passing_yards[:2])
second_half_avg = average(recent_passing_yards[-2:])
if second_half_avg > first_half_avg:
    trend = "up"
elif second_half_avg < first_half_avg:
    trend = "down"
else:
    trend = "flat"

print(f"CSV-based prediction: {prediction:.1f}")
```

Click **Run Python File** again and verify your CSV-based prediction prints.

Expected output (your exact decimal may vary slightly):

```text
CSV-based prediction: 281.4
```

---

### Milestone 6: Build a final prediction card

Now combine prediction, trend, and breakout tag in one clean report block.

Add this below Milestone 5:

```python
tag = breakout_label(prediction, player["season_average_yards"], 30.0)

print("=" * 50)
print(f"PREDICTION CARD: {player['name']}")
print("=" * 50)
print(f"Projected attempts: {projected_attempts}")
print(f"Predicted passing yards: {prediction:.1f}")
print(f"Season average yards: {player['season_average_yards']:.1f}")
print(f"Trend: {trend}")
print(f"Tag: {tag}")
print("=" * 50)
```

Expected result:
- A final card with projected attempts, prediction, season average, trend, and breakout tag.

If you made it this far, you built a complete football prediction workflow from scratch.

---

## Common Fixes

- `FileNotFoundError`: confirm path is exactly `data/recent_games.csv`.
- `ValueError`: check `passing_yards` and `attempts` columns are numeric.
- No play button: install/enable Python extension in Cursor and reopen `performance_predictor.py`.

---

## Bonus Exercises: Push It Further with Your Agent

Use this short prompt structure for better AI help:

1. **Goal**: what you want to build
2. **Context**: file name + what code already exists
3. **Constraints**: keep beginner-friendly, minimal changes
4. **Output format**: ask for exact code + where to paste + expected output

### Bonus 1: Predict Multiple Players

Try this prompt:

```text
You are my Python tutor and coding assistant.

Goal:
Predict passing yards for 3 quarterbacks and print one prediction card per player.

Context:
I am editing performance_predictor.py.
I already have predict_passing_yards(...), breakout_label(...), and a final prediction card format.

Constraints:
- Keep my current single-player flow working.
- Add only what is needed for multi-player support.
- Use beginner-friendly Python.

Output format:
1) Plan (3 bullets)
2) CSV format I should use
3) Exact code changes
4) Where to paste each code block
5) Example output for 2 players
```

### Bonus 2: Add a Prediction Range

Try this prompt:

```text
You are my Python tutor and coding assistant.

Goal:
Show low / medium / high passing-yard predictions instead of one value.

Context:
Current code calculates one prediction from recent passing yards and attempts.

Constraints:
- Keep formulas simple (no advanced libraries).
- Explain the math in one short paragraph.
- Keep my current prediction card and add range lines.

Output format:
1) Range formula and reason
2) Code changes only
3) Expected output block
4) One way to tune the range width
```

### Bonus 3: Compare Prediction vs Actual

Try this prompt:

```text
You are my Python tutor and coding assistant.

Goal:
Compare predicted passing yards to actual passing yards and calculate error.

Context:
I already compute prediction in performance_predictor.py.
I want to add this after the final prediction card.

Constraints:
- Keep this beginner-level.
- Show both signed error and absolute error.
- Do not remove existing outputs.

Output format:
1) Code block to add
2) Where to paste it
3) Expected output with sample numbers
4) One sentence explaining how to interpret error
```


---

## Backlinks

The following sources link to this document:

- [>button: 🏈 Football](/ai-and-sports/projects/6-predicting-the-future/predicting-the-future.project.llm.md)