Playing Tennis like Deep Blue Part 3: The Djokovic Effect

In the Deep Blue Part 2 blog on Computation & Tactics, we reframed tennis as a tactical and strategic process rather than a psychological drama.

In the Tennis Scoreline Probabilities post, we turned that approach into a usable tool: a point‑by‑point model that lets players and coaches manually enter or randomly generate a set, visualize the Point Value of every point, and watch the leverage landscape unfold as peaks and valleys across the graph.

Those peaks aren’t emotional moments — they’re identifiable events that can be leveraged to win matches.

The Point Value model is the backbone of our computational analysis. It shows you exactly where the match can swing, and at the end of each completed set, we now report the Probability Optimization metric (POp), a measure of how much win probability the player actually gained or lost during the set.

This post builds directly on that foundation and introduces the next step: using those Point Values to measure how well a player optimized their chances of winning during a tennis set — a concept we call GTO Tennis Play.

The Leverage Landscape: Tennis as a Value Map

If you’ve used the Tennis Scorelines Probability tool, you’ve already seen the leverage landscape in action. As you enter points, or generate a random set, the graph indicating the Point Value (PV) of each point updates as you go along.

Some points barely change your probability of winning. Others spike upward into steep peaks that can make all the difference.

Those high peaks are the big points not because commentators say so, not because the crowd gets loud, but because the scoring system itself makes them worth more.

For instance, winning or losing the point at 1–1, 40–30 will shift your win probability by 7%, whereas winning or losing the point at 5–5, 30–40 will shift your win probability by 39%.

This is the probability terrain players are actually navigating. And once you can see it, the sport becomes clearer, more structured, and more honest.

The Point Value Model: A Quick Refresher

The Point Value (PV) model calculates, for every point in a set how much the win probability changes:

if the player wins a particular point
versus how much it changes if the player loses that same point

This is the value of each point, the Point Value (PV), which if you think about in terms of a poker game, is the size of the pot that you win or lose with the winning or loss of each point.

You can explore this in the Tennis Scoreline Probabilities tool by either:

manually entering each point for an entire set
or generating a simulated set where every point is randomly determined
and watching the Point Value graph and analyzing the leverage structure of that particular set

You can see the PV for each point either by looking at the highlighted row in the table shown in the background of the tool, or by hovering over the point in the graph.

The peaks in the graph show you where your probability of winning the match can swing the most. The valleys and plateaus show you where your probability of winning the match moves very little.

The Point Value model is the mathematical basis for GTO Tennis Play.

GTO Tennis Play: What It Means (and What It Doesn’t)

In poker, Game Theory Optimal (GTO) play means:

maximizing expected value
allocating your best decisions to the highest‑value situations
avoiding patterns that can be exploited
adjusting aggression based on the size of the poker pot

Although tennis does not have a solved equilibrium strategy, it does have the same underlying structure - a Point Value structure. Players who optimize their play on the Point Value of each point are effectively playing akin to a GTO‑style strategy.

They can maximize the Point Value gained from the points that matter the most.

Some players do this inconsistently. Some do it intuitively. Some, like Djokovic, do it with a conscious and practiced reliability.

And now we have a way of measuring how well a particular player optimizes Point Value during the playing of a match.

Net Point Value: The Player’s Value Ledger

Every time you complete a set in the Tennis Scoreline Probabilities tool, it calculates your Net Point Value (NPV) which is the sum of all the player's wins and losses of PV throughout the entire set.

Winning a "BIG" high‑PV point greatly increases the player's probability of winning the match
Losing a "BIG" high-PV point greatly decreases the player's probability of winning the match

This summation of won and lost PV for a set is the Net PV (NPV): a measure of how many percentage points the player has increased or decreased their win probability during a set compared to their opponent.

This tennis set ledger is the equivalent of a poker session ledger.

Turning Value into Optimization

The amount of Point Value a set contains can vary over a wide range depending on the particular set of scorelines it contains. The Point Value model contains all possible tennis scorelines and therefore contains all of the scorelines within any possible set. This includes every scoreline from the perfect set, where one player wins every point, to all of the possible scorelines leading to a tie break where every point has a Point Value of 50%.

The point is that the leverage landscape for each of these possible sets is different, some sets are full of break points and tight games, while others are filled with routine holds.

To compare performances across sets or matches, we normalize the NPV value, add it to the median value, to give us the Probability Optimization (POp) metric expressed as a percentage.

The POp metric is a clean measure of how well a player converted the leverage they faced.

+100% → captured all available Point Value
50% → broke even, capturing the same amount as their opponent
–100% → gave away all available Point Value

It is not a psychological metric. It’s not a guess about tactics. It’s not a story.

It’s a value‑realization metric - a measure of how close the player came to GTO Tennis Play given the points they actually faced.

The Point Value Model tool currently displays the POp metric for the player at the conclusion of a particular set in the Win/Loss alert. As time allows, we will improve and enhance this presentation.

The Djokovic Effect: GTO Tennis in the Real World

If you want a real‑world example of GTO‑style tennis, look at Novak Djokovic.

He doesn’t magically “rise” on big points. He doesn’t summon clutch out of thin air. He simply:

simplifies patterns
raises execution quality
suppresses errors
forces opponents into low‑PV decisions
and captures a disproportionate share of high Point Value points

The Probability Optimization (POp) metric quantifies this behavior.

It measures how well a player optimized their probability of winning the match. Djokovic consistently scores extremely high and we plan, as time allows, to present that analysis in detail.

That’s the Djokovic Effect — not folklore, just calculable value optimization.

How Coaches and Players Can Use This Today

All of this will be immensely valuable once the BallBOPPer and BallBOPPer App are available, but right now with the Scoreline Probability Table tool, players and coaches can:

enter a real set point by point and see how the player performed on "Big Points"
or generate a random set and compare how Point Value plays out across a wide variety of scorelines
analyze the leverage landscape revealed by the Point Value graph for each set
and get the calculated player's POp metric for a particular set to see how well the player optimized their probability of winning that set

Soon we hope to add the ability to run historical ATP/WTA Grand Slam match assuming we can find the time to implement this in between testing and demonstrating the 2026 BallBOPPer prototype.

This gives you:

a map of where the match actually turned for or against the player
a measure of how well the player handled the big points
a way to evaluate tactical discipline under pressure
a clean, reproducible metric of performance quality

Traditional stats tell you what happened. The PV and POp metrics tell you how much it mattered.