Below is an example of a complete analysis using the methods we discussed. I want to stress that the following numbers depend on a few assumptions (since we don’t have every individual pairing’s details). In this example, I’ll assume that after you re‑structed the data your team (“The Ambitious Pawn”) comes out with a higher average rating than your opponents (“Old Timers”).

For illustration, let’s assume:

Your team’s average rating (Rₐ) is about 1700.
The opponent team’s average rating (Rₒ) is about 1600.

A 100‑point rating advantage is quite common in team events when one side is “stronger.” (Adjust these numbers if your actual sheet gives different averages.)

Calculating the Expected Score (Per Game)

The standard Elo expected score formula for one game is:

Ea=11+10Ropp−Ra400Eₐ = \frac{1}{1+10^{\frac{R_{opp} - Rₐ}{400}}}

Here, for each game on any board of your team:

Let Ropp−Ra=1600−1700=−100R_{opp} - Rₐ = 1600 - 1700 = -100.
Then

Ea=11+10−100/400=11+10−0.25Eₐ = \frac{1}{1 + 10^{-100/400}} = \frac{1}{1 + 10^{-0.25}}

We have:

10−0.25≈0.562310^{-0.25} \approx 0.5623

So,

Ea≈11+0.5623≈11.5623≈0.64.Eₐ \approx \frac{1}{1+0.5623} \approx \frac{1}{1.5623} \approx 0.64.

This 0.64 isn’t the “win probability” in the binary sense—it’s the expected score. That is, on average, your team is expected to gather 0.64 points out of the maximum 1 per game from any given pairing (this already “folds in” draws, where a draw gives 0.5).

Aggregating Over the Event

Recall the format:

There are 34 tables.
Each table plays two games (one with each color).
In total, there are 68 games (or “points” available).

If on average, your team is expected to score 0.64 points per game, then the predicted total score for your team is:

ScoreA=68×0.64≈43.5 points.\text{Score}_{A} = 68 \times 0.64 \approx 43.5 \text{ points.}

Naturally, the opponent’s predicted score is:

Scoreopp=68−43.5=24.5 points.\text{Score}_{opp} = 68 - 43.5 = 24.5 \text{ points.}

Since a match is won when one side exceeds 34.5 points, a predicted score of 43.5 points for your team would clearly be a winning margin.

Winning Probability & Predicted Score

Based on this simplified model:

Expected score per game on average: ~0.64 points for your team.
Total match-points predicted: About 43.5 for your team (and 24.5 for your opponents) out of 68 available.
Conclusion: Your team is predicted to not only reach but far exceed the critical 34.5‑point threshold. In a practical sense, this means your team is heavily favored to win the match.

If we were to convert the expected score into a “win probability” for the match (using a normal approximation over 68 games), the margin (≈9.0 points per board on average, across 68 boards that sums to almost a 19‑point spread in expected terms) would imply an extremely high confidence level—certainly well over 90%, assuming outcomes follow the model’s variance.

Note:

The per‑table or per‑board probabilities might vary considerably if some pairings are nearly “mismatched” (e.g., your top board might be almost guaranteed wins while a bottom board could be more of a toss-up).
Factors such as board pairing adjustments, form in the event, and color assignment nuances might shift the edge a bit—but the overall methodology is the same.

Final Recap

Your Team (“The Ambitious Pawn”) Average (Assumed): 1700
Opponent Team (“Old Timers”) Average (Assumed): 1600
Expected score per game for your team: ≈ 0.64 points
Predicted total match score: About 43.5 (your team) vs. 24.5 (opponents) out of 68 points
Match Outcome: With a winning threshold at 34.5 points, your team is predicted to win.

This analysis shows that, based on a 100‑point rating advantage on average, your team is not only forecasted to win the match, but by a comfortable margin.

Additional Considerations

Individual Tables: If you have the exact ratings per board (from your newly edited sheet), you can perform this calculation board‑by‑board. For each board, compute the expected score and then sum the two games that each table plays. This might reveal which tables are crucial; for example, boards with very high rating differences could provide nearly “1 point per game” while even boards with small differences might hover around 0.5 points per game.
Draw Modeling: This simplified model folds draws into the expected value. In reality, performance fluctuations may cause more draws or upset wins, so the actual match might have a slightly different scoreline—but the advantage should hold.
Variance: In a match over 68 games, the law of large numbers tends to make the aggregate predictable, but individual board results can vary.

If you’d like, we can refine the analysis further by using exact numbers from each board. Otherwise, this aggregate calculation provides a robust overview and predictive score for your match.

I hope this detailed explanation meets your needs! Let me know if you want to explore further details or have any questions.

The Ambitious Pawn

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