When people hear “AI in tennis,” they usually jump to the same conclusion.

Predictions.

Who’s going to win, who’s in form, who has the better stats. It all sounds very straightforward — almost too simple. Like the game can be reduced to a few numbers and a quick answer.

But if you’ve watched enough tennis, you already know it doesn’t work like that.

Matches don’t follow a script. Players don’t perform the same way every time. And sometimes the guy who looks completely in control ends up losing anyway.

So the real question isn’t whether AI can predict tennis.

It’s how it actually tries to understand it.

Tennis isn’t built for simple answers

Unlike team sports, tennis looks clean on the surface.

No formations. No teammates. No complex systems — at least not visibly.

But that simplicity is misleading.

Because every match is a mix of small factors happening at the same time. Serve quality, return position, rally length, confidence, surface, opponent style — and all of it changes constantly.

That’s why simple stats only go so far.

You can know that a player serves at 65%, but that doesn’t tell you how effective those serves are. You can see the number of winners, but not how those points were actually created.

And that’s where things start to get complicated.

AI doesn’t “see” the match like we do

When we watch tennis, we rely on instinct.

We notice body language. We feel momentum. We react to big points. Sometimes we’re right, sometimes we’re completely off.

AI doesn’t work like that.

It doesn’t care about the score in the same emotional way. It looks at structure — what keeps happening, not what stands out once.

Instead of focusing on one big point, it looks at hundreds of smaller ones.

How rallies develop. Where points start. What happens after the serve. Which patterns repeat.

And that’s a completely different way of looking at the game.

It’s about patterns, not highlights

If there’s one thing AI does better than humans, it’s tracking repetition.

In tennis, that matters more than anything.

A player might hit one incredible winner, but that doesn’t tell you much. What matters is what they do ten times in a row.

Do they keep going to the same side? Do they struggle when rallies get longer? Do they rely too much on their first serve?

These are patterns.

And once a pattern becomes clear, it usually decides the match.

Where platforms like this come in

This is exactly where tools like TennisPredictions.ai start to make sense.

They’re not just collecting stats.

They’re organising them in a way that shows how a match is actually played underneath the surface.

Instead of giving you isolated numbers, they try to connect everything — serve, return, rally, outcome — into something that resembles the flow of a real match.

And that’s the key difference.

AI doesn’t simplify tennis — it makes it clearer

A common mistake is thinking AI simplifies the game.

In reality, it does the opposite.

It shows how complex tennis actually is.

When you start looking at matches through patterns instead of just points, you realise how many small things are happening at once. And how those small things build into something bigger.

It’s not about removing uncertainty.

It’s about understanding where that uncertainty comes from.

The moment a match starts to shift

One of the most interesting things about tennis is how matches change without you noticing immediately.

The score might stay even.

But something underneath is already different.

Maybe one player is returning deeper. Maybe rallies are getting slightly longer. Maybe the serve is no longer creating easy points.

These are small changes.

But they repeat.

And when they repeat, they become patterns.

That’s usually when the match starts to turn — long before it shows on the scoreboard.

Why humans struggle to track all this

The problem isn’t that fans don’t understand tennis.

It’s that there’s too much happening at once.

You can’t track every rally, every pattern, every adjustment in real time. You focus on what’s in front of you, and the rest fades into the background.

You might feel that something is changing.

But explaining it clearly is another story.

That’s where AI helps — not by replacing your view, but by filling in the gaps.

It’s not about being right every time

Another misconception is that AI should always be correct.

That’s not the point.

Tennis is too unpredictable for that.

What matters more is whether the reasoning makes sense.

If you understand why a match is leaning one way — even if it doesn’t end that way — you’re already seeing the game at a deeper level.

And that’s a big step forward compared to just reacting to the score.

The difference between watching and reading a match

There’s a subtle difference between watching tennis and reading it.

Watching is reactive.

Reading is about understanding how things connect.

Once you start noticing patterns, you move from one to the other.

You don’t just see a missed shot — you see what led to it. You don’t just see a break of serve — you see the pressure building before it happened.

And that changes everything.

Why this matters for fans

At the end of the day, most people don’t care about AI itself.

They care about the match.

But the more you understand what’s happening, the more interesting the match becomes.

You’re not just waiting for big moments anymore. You’re following the build-up to them.

You see why things happen, not just that they happen.

Tennis is still unpredictable — and that’s the point

Even with all this, tennis will never be fully predictable.

There will always be moments that don’t fit the pattern. A sudden mistake. A shift in confidence. A match that goes in a completely unexpected direction.

And honestly, that’s what makes it worth watching.

AI doesn’t remove that.

It just helps you understand everything around it a bit better.

Conclusion

AI in tennis isn’t about replacing instinct or taking the human side out of the game.

It’s about seeing more of what’s already there.

The patterns, the repetition, the small details that build into something bigger.

Because once you start noticing those things, matches stop feeling random.

And start feeling like something you can actually read.

Yes, we know that this is taking a while!

As many of you know, we at Notinhalloffame.com are slowly generating the top 50 of each major North American sports team. That being said, we maintain and update our existing Top 50 lists annually.  As such, we are delighted to present our pre-2026 revision of our top 50 Colorado Rockies.

As for all of our top 50 players in baseball, we look at the following: 

1.  Duration and Impact.

2.  Traditional statistics and how they finished in the Major League Baseball.

3.  Advanced Statistics.

4.  Playoff performance.

5. Their respective legacy on the team.

6. How successful the team was when he was there.

7. Respecting the era in which they played.

Criteria 1-4 will make up the lion’s share of the algorithm.  Please note that we have implemented this for the first time.  This has changed the rankings all throughout the board.

Last year, the Rockies were the worst team in baseball and won only 43 Games.  Even though the team has not been around long, the Rockies’ awfulness last year meant no new entrants, though some active players moved within the list.

As always, we present our top five, which differs from last year's, due to the new algorithm.

1. Todd Helton
2. Larry Walker
3. Nolan Arenado
4. Troy Tulowitzki
5. Charlie Blackmon

You can find the entire list here.

With the new algorithm, Troy Tulowitzki and Charlie Blackmon flipped their respective #4 and #5 ranks.

We thank you for your continued support of our lists on Notinhalloffame.com.

1993 PRELIMINARY RESULTS:

Thank you to all who participated in the Pro Football Hall of Fame Revisited Project. If you are still determining what that is, we treated the PFHOF as having its first class in January 1946.

We have completed the first 47 years.

For “1993,” a Preliminary Vote with over 100 players whose playing career ended by 1987. We also follow the structure in which players have 20 years of eligibility, and if they do not make it into the Hall, they are relegated to the Senior Pool.

Each voter was asked to select 25 names from the preliminary list, and the top 25 vote-getters were named Semi-Finalists.

A week later, voters will be asked to select 15 names from the 25 Semi-Finalists, then choose five from the remaining 15. We will continue this process every week until we catch up to the current year.

Please note that a significant change occurred “years ago,” allowing voters to submit fewer than the allotted spots. 

31 Votes took place.

This is for the “Modern Era”

Bold indicates they advanced to the Semi-Finals:

*Indicates they have been removed from future ballots

This is for the Senior Era

Bold indicates they advanced to the Semi-Finals:

*Indicates that they will be removed from the ballot permanently.

This is for the “Coaches/Contributors”

Bold indicates they advanced to the Semi-Finals:

*Indicates that they will be removed from the ballot permanently.

Next week, we will announce the Semi-Finalists for the 1993 Pro Football Hall of Fame Revisited Project.

Yes, we know that this is taking a while!

As many of you know, we at Notinhalloffame.com are slowly generating the top 50 of each major North American sports team. That being said, we maintain and update our existing Top 50 lists annually.  As such, we are delighted to present our pre-2026 revision of our top 50 Cleveland Guardians.

As for all of our top 50 players in baseball, we look at the following: 

1.  Duration and Impact.

2.  Traditional statistics and how they finished in the Major League Baseball.

3.  Advanced Statistics.

4.  Playoff performance.

5. Their respective legacy on the team.

6. How successful the team was when he was there.

7. Respecting the era in which they played.

Criteria 1-4 will make up the lion’s share of the algorithm.  Please note that we have implemented this for the first time.  This has changed the rankings all throughout the board.

Last year, the Guardians were sellers at the trade deadline but got hot and shocked the baseball world by driving toward an AL Central Division Championship.  They fell to Detroit in the Wild Card, but considering where they were on August 1, this was a positive campaign for Cleveland. There were no new additions, but movement within the Top 50.

As always, we present our top five, which differs from last year's, both due to the new algorithm and to one active Guardian.

1. Bob Feller
2. Nap Lajoie
3. Tris Speaker
4. Jose Ramirez
5. Lou Boudreau

You can find the entire list https://www.notinhalloffame.com/baseball/top-50-baseball-players-by-franchise/top-50-cincinnati-reds">here.

Major changes were afoot among the top five, as the new algorithm flipped Bob Feller and Nap Lajoie from their previous #1 and #2 spots. 

Off the strength of another All-Star campaign, Jose Ramirez broke into the top five.  He was ranked at #7 last year.

The only other change among active Guardians players was the return of Carlos Santana, who rose from #41 to #37.  He is now with the Arizona Diamondbacks.

We thank you for your continued support of our lists on Notinhalloffame.com.