How Our Prediction Tool Works

Understanding how we predict game scores using OSPR (Ohio Sports Power Rating) and team statistics

Note: Our predictions are powered by OSPR (Ohio Sports Power Rating) , which combines CalPreps (football), ELO, and Colley methods for maximum accuracy.

Our prediction tool uses a hybrid approach that combines OSPR (Ohio Sports Power Rating) with actual team statistics to predict game scores. Unlike simple win probability calculators, our tool predicts the actual score of the game, taking into account offensive and defensive matchups, home advantage, and team strength across all sports.

What Makes Our Tool Unique

Real Scoring Data: We use each team's actual points per game and points allowed per game, not just theoretical ratings.

Defensive Matchups: We consider how each team's offense matches up against the opponent's defense.

OSPR Adjustments: Team strength (via OSPR) fine-tunes the predicted scores.

Home Court Advantage: We account for the proven advantage of playing at home.

The 5-Step Prediction Process

Calculate Win Probability

First, we use OSPR ratings to determine each team's probability of winning. OSPR combines three proven systems (CalPreps for football, ELO, and Colley) for the most accurate strength assessment. Home field/court advantage is built into the OSPR calculation.

Win Probability based on OSPR difference and tier comparison

OSPR accounts for rating gap, tier classification, and momentum trends

Gather Actual Scoring Statistics

We pull each team's season statistics from their actual games:

Points Per Game (PPG): How many points they typically score
Points Allowed Per Game (PAPG): How many points they typically give up

Note: If a team hasn't played any games yet, we use default averages (boys: 55 PPG, girls: 48 PPG).

Analyze Defensive Matchups

This is where it gets interesting. We don't just use each team's average - we consider how their offense matches up against the opponent's defense:

Team A's Expected Score:

(Team A's PPG + (Team B's PPG - Team B's PAPG)) / 2

This formula answers: "If Team A plays a defense that typically allows X points, and their offense averages Y points, what's a realistic expectation?"

Apply OSPR Score Adjustment

Better teams should score more points. We adjust the predicted scores based on OSPR rating difference:

OSPR Score Adjustment based on rating gap and tier difference

OSPR ratings range from 0-100, with tier classifications (S/A/B/C/D/F) providing additional context. A higher OSPR rating translates to a scoring advantage.

Example: If Team A has a 15-point OSPR advantage (e.g., 75.0 vs 60.0), the spread and scoring predictions favor Team A proportionally.

Add Home Court Advantage

Finally, we give the home team an additional 2-point boost to their predicted score. This is separate from the ELO adjustment and reflects the proven advantage of:

Familiar court and surroundings

Supportive home crowd

No travel fatigue

Referee familiarity

Example Prediction

Let's walk through a real prediction scenario:

Team A (Home)

• ELO Rating: 1600
• Points Per Game: 58
• Points Allowed: 52

Team B (Away)

• ELO Rating: 1500
• Points Per Game: 54
• Points Allowed: 55

Step 1: Team A's adjusted ELO = 1602.5 (1600 + 2.5 home advantage), ELO difference = 102.5 → Win probability: ~61%

Step 2: Statistics gathered from actual games played this season

Step 3: Team A expected: (58 + (54 - 55)) / 2 = 57.5 | Team B expected: (54 + (58 - 52)) / 2 = 54.5

Step 4: ELO adjustment = 102.5 × 0.12 = 12.3 → Team A +6.15, Team B -6.15

Step 5: Team A +2 home court points

Final Prediction: Team A 66 - Team B 48

18-point predicted margin

Understanding Your Prediction Results

Predicted Score

This is our best estimate of the final score based on all available data. Remember, basketball games are inherently unpredictable - injuries, hot shooting nights, and momentum can all affect outcomes.

Win Percentage

This shows the probability of each team winning based purely on ELO ratings. A 70% win probability means that team would be expected to win 7 out of 10 games against this opponent.

Predicted Margin

The point difference between the two teams. A larger margin indicates a more lopsided matchup, while a margin of 5 points or less suggests a close, competitive game.

Prediction Confidence

Shows how reliable our prediction is based on the amount of game data available for both teams. More games played means more accurate predictions.

High Both teams have 10+ games - very reliable prediction

Medium Teams have 5-9 games - reasonably reliable prediction

Low Teams have 3-4 games - limited data reduces reliability

Very Low Teams have 1-2 games - use prediction with caution

Note: The confidence is primarily determined by the team with fewer games played. Early in the season, expect lower confidence ratings.

How ELO Ratings Evolve During the Season

Our system uses sport-specific dynamic K-factors that adjust how much each game affects a team's rating. This means early season games have more impact than late season games. Different sports use different schedules based on typical season length.

Football (10-game season)

Football uses a faster progression since teams typically only play 10 regular season games:

Games 1-2 (Quick Initial Adjustment)

K-Factor: 140% - Ratings adjust quickly in the first two games

Games 3-5 (Moderate Learning Phase)

K-Factor: 120% - Building confidence with moderate volatility

Games 6-10 (Stable Ratings)

K-Factor: 100% - Ratings stabilized for the second half of the season

Basketball, Baseball & Softball (20-30+ game seasons)

These sports use a gradual progression since teams play many more games:

Games 1-3 (Very High Impact)

K-Factor: 160% - Ratings change significantly as we learn about team strength

Games 4-5 (High Impact)

K-Factor: 140% - Still building confidence in the ratings

Games 6-8 (Moderate Impact)

K-Factor: 120% - Ratings becoming more stable

Games 9+ (Standard Impact)

K-Factor: 100% - Ratings are well-established and change moderately

This sport-specific approach ensures that ratings adapt appropriately for each sport's season length, reaching stability at the right point in the season.

Important Considerations

• Early Season: Predictions show lower confidence when teams have played fewer games, but ratings adapt quickly thanks to dynamic K-factors
• No Team Data: If a team hasn't played any games, we use league-wide averages, which may not be accurate
• Injuries & Roster Changes: Our tool doesn't account for player injuries or recent roster changes
• Momentum: Recent hot or cold streaks aren't weighted - we look at full season averages
• Tournament Games: Playoff pressure and neutral sites can affect outcomes differently than regular season games

Why Use a Hybrid Prediction Model?

Pure ELO systems only tell you who is likely to win, not by how much. Our hybrid model gives you both:

Win Probability (from ELO)

Shows which team is stronger overall and their likelihood of winning

Score Prediction (hybrid)

Predicts the actual scoreline by combining team strength with offensive/defensive stats

This gives coaches, players, and fans a more complete picture of what to expect in a matchup.

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