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Fifteen schools and 113 players competed at Pickle Haus this past weekend to be crowned the Illinois Super Regional champions. By player count, it's the largest Regional that DUPR has ever run and the 3rd largest collegiate pickleball tournament ever! Multiple news stations were there to cover the action, as well as plenty of spectators from the surrounding Chicago area.

This tournament was the competitive debut for the UW-Madison, SIUE, Carnegie Mellon, Cornerstone, and Concordia pickleball teams. At the top, we saw the familiar faces of Ohio State, TCU, Indiana, and Michigan reach the semifinals and claim a bid to Nationals in November.

At our Michigan Regional last year, we saw Indiana beat the Ohio State 1st team in a Dreambreaker to advance to the finals and win the whole tournament. However the tables turned at the Illinois Super Regional, and it was instead the Ohio State 1st team getting their revenge and beating Indiana in the Dreambreaker to advance to the finals and winning the whole tournament.

The Illinois Super Regional was the first of eight Super Regionals DUPR is running this year, with the next one being the Utah Super Regional in Kaysville, Utah on March 2nd-3rd.

A big thank you goes to our sponsors: HEAD, GAMMA, OOFOS, Michelob Ultra, and Total Pickleball.

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DUPR (Dynamic Universal Pickleball Rating) is thrilled to announce the expansion of its partnership with GAMMA Sports, marking an exciting new chapter in the future of pickleball in 2024. GAMMA is set to become the official ball for DUPR with the launch of the innovative CHUCK Tournament Pickleball to the market.

“We are thrilled to be expanding our DUPR partnership. DUPR’s commitment to pickleball aligns perfectly with our brand mission to expand the pickleball community. We believe this collaboration will support growth and introduce pickleball to an even wider audience,” said Molly Boras, Executive Vice President of GAMMA Sports.

"This strategic partnership with GAMMA Sports as The Official Ball of DUPR is truly exciting. Together, we look forward to strengthening our joint mission of growing the sport of pickleball at a rapid rate,” said Ben Van Hout, Director of Sponsorships atDUPR.

The CHUCK Tournament Pickleball will make its debut at DUPR's Collegiate Super Regional on February 3rd and 4th in Illinois. The ball features 38 holes instead of 40 for a guaranteed true flight and is engineered to withstand temperatures at both ends of the outdoor spectrum. The CHUCK Tournament Pickleball will be released for sale on February 7th on GAMMA Sports website.

As two entities committed to advancing the sport of pickleball, DUPR and GAMMA are excited about the potential of this partnership in fostering community engagement amongst all pickleball players in the nation and on a global scale.

To learn more about DUPR, please visit: www.mydupr.com

To learn more about Gamma Sports, please visit: www.gammasports.com

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About DUPR:

DUPR (Dynamic Universal Pickleball Rating) is the premier global pickleball rating system and technology platform, trusted by the world's leading clubs, tournaments, leagues and players. DUPR's dynamic rating system unifies pickleball across age, gender and location by analyzing match results to accurately evaluate all players across a 2.000 - 8.000 scale.

For more information on DUPR, visit the official website and follow DUPR on Instagram, Facebook, Twitter, and TikTok.

About GAMMA Sports:

GAMMA Sports is a family-owned manufacturer of innovative pickleball and tennis equipment.  With over 50 years of experience in racquet sports, GAMMA has become a trusted name among athletes and sports enthusiasts around the world.  With a commitment to quality and performance, GAMMA provides products to improve every player’s game. The company's product lineup includes high-performance paddles, balls, grips, and accessories.

GAMMA Media Contact:

Paige Powers

Communications Manager, GAMMA Sports

Email: paige.powers@gammasports.com

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Explaining the Research Behind DUPR’s Jan. 30th Updates

You all have probably seen the quick-hitter algo update and FAQs we posted on Jan 25th looking forward to the Jan 30th algo improvements or saw me talk briefly about them in my Reddit post last week. I wanted to take the opportunity to go a little more in depth here for those of you who are interested as to how we took the methods from my last blog post and applied them to these decisions, or just generally for those who are keen to get a better understanding of the algo’s inner workings.

Our goal is, in essence, to solve an “optimization” style problem: maximize X while obeying Y. In our case here, “X” is the accuracy of the overall system (we want to predict future matches as accurately as possible) and “Y” is the standard of pre-match transparency and post-match interpretability (it should be intuitive to a user whether their DUPR will go up, down, or stay the same as a result of the match they just played).

The original weekly algo had high accuracy and low transparency/interpretability —it did well on the optimization but didn’t satisfy the requirements. The instantaneous algo had lower accuracy but high transparency/interpretability — it easily satisfied the requirements but with plenty of room to grow on accuracy.

I see transparency as having two key components:

1. Knowing What Caused a Move
2. Intuitive Movement Results

These components are crucial for users because they build trust in the system through clear and justifiable results, ensuring the output is viewed as fair and reliable for all.

The instantaneous ratings update presented a solution to the first transparency component by effectively telling the user: “this match, and only this match, is the reason your DUPR just changed”.

The win-and-go-up aspect of the instantaneous rating was an initial solution to the second transparency component because, while you may not know the exact amount a given match result will move your DUPR, you can at least have a good feel for the directionality and scale on intuition by comparing everyone’s ratings and the simple final W/L result.

So, how do we improve our accuracy while staying within the defined bounds? DUPR can either operate more accurately within the current solutions to these constraints (by building better models with the same requirements of instant results and win-and-go-up movements) or it can build additional solutions by adding channels of communication which show that causality or intuition more clearly to users.

Given some workflow hurdles to solve for the fact that many users typically only look at DUPR after the match has been played, and the required technical lifts to build these communication channels, our January 30th algo update is focused on optimizing within the solutions available to us, making sure each step adds accuracy and intuition.

Win Intensity

Win intensity is stage one of fully reintroducing points to our ecosystem in a transparent and effective way. The question becomes: how do we reintegrate the accuracy of counting points back into the rating such that it satisfies the intuitive movement constraint?

The first step is: does counting points actually improve accuracy? Does seeing someone win 11-8 vs winning 11-0 mean that our prediction of their future performance should be relatively lower in the 11-8 case? With the framework discussed in the last blog, we can now answer this question pretty explicitly. We spin up a version of the algo that scales the movement of a player based on the points they’ve won, and, as many of you may have guessed, points do in fact add predictive value!

Calculating all of the points, even without much additional sophistication, adds an additional 25% or so of accuracy!

But there’s a catch. If Albus was supposed to win 11-5 against Barnabus and instead Albus only won 11-8, this pure points model would actually indicate Albus’ rating is too high as it currently stands or else it would’ve known to predict 11-8 from the get go! So the result would nudge both Albus’ and Barnabus’ ratings closer to the ratings that would’ve predicted 11-8: Albus goes down and Barnabus goes up, despite Albus beating Barnabus. While Barnabus may love the fact that his rating went up “unexpectedly”, Albus certainly could be frustrated. Maybe he had the game in hand at 11-4 and went easy for the last few points because he didn’t know he had to win 11-5 or better. Without a way of communicating that 11-5 target to users before the match is played, we simply don’t feel comfortable imposing that negative experience on users at this stage. We’re working on making this possible, but since many users don’t enter matches until after the match completes, for instance, we will have to address the entire user workflow to make this available for us.

So what can we do now? Well, we can design another model that still uses points, but allows for the inflection to still be at whether you’ve won or lost. By tuning the hyperparameters around exactly how this model works, we can maximize the accuracy of this method to within 10% of the pure points model — a significant step forward from where we are now! We’re calling this model feature Win Intensity. As before, if you win a match, you will go up, and if you lose, you will go down —this is the only way to guarantee transparency with this process because of the above.

But, now when you win by just a little, your rating barely moves up. If you win by a lot, your rating moves up much more. More of that point spread information is being incorporated.

Representative example of how DUPR can use Win Intensity to get close to the Pure Points-based ratings movement while still having an intuitive behavior. The exact shape here is for illustrative purposes only.

So yes, if I hypothetically took a bunch of lessons and managed to lose to Ben Johns by only 11-9, I would move down a very, very, very small amount after that match even though I would ideally show as having played quite above my current station. But when I turn around and start beating other people now by increasingly larger spreads, my rating would begin to rise much faster than in the former format. With more matches played, the relative differences from the Win Intensity model get averaged out and have less and less of an influence.

This is an area of ongoing focus for us, of course. We’d love to have all 25% of that accuracy bonus back (and then some), but this is a significant step in a positive direction while staying within the transparency conditions that have generated a significantly more active user-base.

Match Recency

I promise the next few sections will be shorter!

On the topic of taking a bunch of lessons and getting significantly better, we needed a way to incorporate drift and movement in player skill. Players get better with practice, more focus, or just generally more experience. Maybe they’re transitioning from another racquet sport and picking up a new pickleball-specific skill every week they touch a paddle. In the pre-Jan 30th instant algo, movement was constant regardless of when the games were played.

In this release, the ratings will now move proportionally to their match recency weighting. Recent matches will count for more in your rating and their weights will begin to slowly drop over time. As you get further and further from that match, the more recent matches entered will then hold relatively higher emphasis on your rating. If you take a few months off, the matches in your account will continue to decay in weight. Once you start entering matches into DUPR again, those recent matches will be much fresher than the old ones and your rating will move more than if you had been playing every day leading up to your latest match. This helps us learn better particularly in the case where you’ve taken some time off to practice and are coming back into the game with a few new tricks up your sleeve!

How did DUPR come up with the particular shape and decay rate? You may even be able to guess my answer at this point! We built a few hypotheses based on statistical theory and experience, fit them into our model, ran them against our dataset, and the one that best captured the natural trend in player skill movement was selected!

Match Count

The last big change that was burning a hole in our quantitative pockets for this Jan 30 release date was the idea of match count and learning rate. Imagine you flipped a coin one time and have no prior knowledge of coins. They are an absolute mystery. It landed on heads. Our best guess at this time is that it would always land on heads–we have no idea what else it could be since we’ve only ever seen one flip! The next time we flip it, it lands on tails, and our best guess is now that it's only a 50/50 chance of being heads since we’ve now observed one of each. That's a move of 50 whole percent! On the other side of the spectrum, imagine we had flipped a million coins in our life and we had meticulously recorded 500k heads and 500k tails. Our next coin flip, be it heads or tails, doesn't change our estimate that much as to how likely this coin is to land on heads; we've already seen so much information to support our hypothesis that coin flips are 50/50 that this next flip adds relatively little new information.

Playing pickleball acts statistically in quite the same way; the more you play, the more we know about you and the less new information we gain from the latest match. An MLP/PPA pro is probably pretty close to their true rating, whereas someone just joining DUPR has a lot of unknown variables to discover! You may think “Wait, every time I play you should be learning more about me, right?” That is still true! But if it was your first game ever on DUPR, we’d have 100% of our knowledge about your rating coming from one single match and would have to update accordingly. As you play more matches, there are certain pieces of information that are redundant from match to match, and less and less of the match’s details are “surprises” to the algo that it can learn from. Let’s say you play the same team 10 times in a row. By the tenth match, we probably have a pretty good idea of how you typically match up against them and that tenth match usually just helps to confirm that constructed view rather than forming a new view altogether.

But you’ll notice this effect is acting in the opposite direction of the Match Recency effect; where Match Recency says we learn more from recent matches, Match Count says we are learning relatively less. With our new framework, we can calibrate these two effects together to determine the appropriate rates for DUPR to both “learn” and “forget” to maximize the predictive nature of the ecosystem.

Transition

With Win Intensity, Match Recency, and Match Count all affecting the ratings as of January 30th, how are matches entered prior to then going to be adapted into the upgraded system? This especially becomes a technological and operational concern when we have to go back and amend old matches or merge player accounts. The best way for us to do this, while also getting the benefit of getting that 15% accuracy bump as soon as possible, is to batch fix everyone’s ratings to the new model’s best guess. This, as you might imagine, would cause a lot of people to be quite frustrated, particularly those of you who may be slightly higher in the old system vs the new.

Our compromise is going to be to batch adjust only those people’s ratings whose ratings are going to go UP in the updated model. If you notice your rating has jumped overnight, congratulations! The upgraded model likes you more than the prior version did! This definitely will create some bias in the average rating since we’re not equivalently moving others down, but as we move further and further into the future, this bias will get decayed through the Match Recency effect and players will move to their unbiased ratings as they play.

Thank You for Reading!

With these updates to the DUPR algorithm, we've focused on making the system not only more accurate but also more intuitive and accessible, ensuring that every DUPR user can easily understand and benefit from their personal rating progression. We’re excited about these changes and the process through which they resulted and hope you’ve enjoyed seeing a little bit more into how we came to the decisions we did. As we work towards future algorithm updates and improvements, or maybe just as interesting topics pop up in the space, I hope to see you back here with me!

Written By: Scott Mendelssohn - Head of Analytics, DUPR

New-DUPR’s Data-Driven Approach

Pickleball, especially the professional pickleball landscape, has gone through a tumultuous period of late. A continuing surge in the growth of the sport paired with the ever-present “will they/won’t they” relationship between the MLP and the PPA saw DUPR spun out from its former MLP umbrella and into a verdant green pasture of new ownership and an opportunity to fully take the reins on our own destiny.

With the changing of the guard, DUPR can refocus on what it can do best—provide universal, ageless, and genderless ratings for recreational players and professionals alike to foster competitive matches, ensure honest tournament entries, and find partners to play with or against no matter where in the world you are.

You’ve heard that story before and maybe you’ve bought in and are along for the ride or maybe you’ve been burned before and are feeling a little skeptical. This series of blog posts I’ll be writing in the coming weeks is for both of you as we build up to the release of our first algo change reintroducing the points effect since THE algo change back in June. For those of you with your DUPR-branded bucket hats already atop your head, this is a chance to get a peek behind the scenes at what a real research and sports analytics team looks like now that we can be, first and foremost, a research and sports analytics team. For those of you remaining dubious, I hope this provides insights into how we learn from the past, how we approach the problems to solve, and can demonstrate that, while we are certainly ever-imperfect (as all science is), we take this responsibility seriously and have the process in place to deliver results.

A History and What We’ve Learned

For a relatively young company, DUPR has certainly seen its fair share of iterations. I’m sure there are more nuanced stratifications to be drawn, but from an algorithmic side, we’ve basically lived 2 lives:

1. Once-Per-Week Ratings Update
2. Instant and Transparent Results

Each of these algorithm styles has its pros and cons that need to be balanced across all of the company’s objectives–namely, in our case, user experience and accuracy. Let’s dive in.

Once-Per-Week Ratings Update

The original algo, characterized by its update every week, moved slowly. For many of our enthusiastic players eagerly participating in matches throughout the week, the anticipation of seeing their DUPR rating update every Tuesday was a unique experience but lacked the benefits of instant gratification. Further, by combining the effects of an entire week’s performance into one single ratings move, it was hard to understand the effects and build faith in its quality. More often than not, the algo was working “as intended”, but there was absolutely no way for you to go back and verify, leading to skepticism and distrust. Clearly, the user experience needed an overhaul.

But “working as intended” for this algo was also quite raw; in places where the model couldn’t completely solve a problem, heuristics were used to hold the ship together. This is not altogether uncommon. In many algorithms across industries, heuristics are used to optimize a secondary constraint: labor-hours. If you can put something out into the world that moves the needle in a positive direction, the next biggest improvement may be had by going out and finding a second or third needle to positively move rather than spending far more time on needle number one. As long as you’re not looking for these needles in a quantitative haystack, you’re probably better off going out and finding one. When DUPR started, the problem of accurate pickleball ratings was less of a quantitative haystack and more of a quantitative needle-stack.

One of the needles we uncovered ended up being crucially important: connectivity.

How do you accurately compare Player A to Player B if they’ve never met, and may not even be in the same state or even country?

What happens as those connections grow or change? The full deep-dive into this thought process is worthy of its own blog post (and it will absolutely be one down the line when we also talk about ratings reliability!), but the important visualization here is that pickleball is a web of relationships, and information flows through these relationships in a definable and studyable way. Unlike most other sports with highly connected infrastructures that ship their players around the country to play each other in person (football, basketball, hockey, soccer, etc.,) or those with online presences that can generate artificial proximity (online chess and other forms of E-Gaming), pickleball is still relatively disjointed and communal.

A graphical representation of every doubles pickleball match on DUPR. Each node is a player and each line is a match, clustered by strength of connection.

The initial connectivity solution provided a significant boost in the accuracy of the system, but without the tools in place at the time to explain it, it contributed to effectively all of the confusion among the users of that system.

We understood this confusion was a significant issue in the original approach and demanded a change, but the complexities of this transition, both mathematically and technically, were immense.

Instant and Transparent Results

Faced with tight deadlines and a focus on immediate solutions, the team worked diligently to address emerging issues. This rapid pace naturally placed significant demands on the tech stack, particularly when managing the data infrastructure requirements of large-scale, instantaneous updates for the entire population. The initially simplistic effects of an instantaneous algo and the prerequisites around things like unrated player initialization had to be solved in real time after going live. Eventually, after having whacked all of the moles, we got ourselves back to a steady state and were able to take a breath and try to take a pulse on the ecosystem at-large.

One thing was incredibly clear.

Despite the bugs, engagement was up with DUPR. Like, UP up. Getting immediate and transparent results was so gratifying to the user base as compared to the previous version that we saw the fastest growth in match entry per user than we had ever seen and new users were flooding the platform at the highest rate in company history.

Being able to know that “winning meant going up” gave players a concrete DUPR goal that aligned with their pickleball goal for a given match. Whereas the population of users (mostly) understands and believes that accounting for every single point would be more accurate, there was no denying that having no idea the amount of points you’d need to score in a given match to go up could lead to frustrations, especially if it was never explained why a rating had changed after the fact!

Immediacy was fun and transparency was key.

But the transition was bumpier than we had hoped for and the algorithm needed a few rounds of iteration until we got back to the plant-wide accuracy we expect from DUPR.

Going Forward

We knew we couldn’t move away from immediacy and transparency, and obviously we needed a way to reintroduce that crucial element of connectivity. So now, with the ability to forge our own research and development path as we see fit, the question becomes: How do we generate the most accurate possible system within the constraints of that user experience? Well, for that, we first need to understand what we mean by accuracy.

How We Measure Accuracy

I ask you to predict the outcome of a coin toss and the chance that it ends up on heads. (I also ask that you bear with me for some very light math… I promise). Common sense has likely given you the answer to the question already: 50%. Imagine you are asked to evaluate two different models that are in the business of predicting coin tosses (if you have a good one, please let me know and we can make some Super Bowl Prop Bets together this year…).

1. Model 1 says there is a 55% chance of landing on heads
2. Model 2 says there is a 100% chance of landing on heads

When comparing these two models, both of them guess “heads” every time and get the answer right 50% of the time. Yet, clearly, both of these models are “wrong”. The right answer to guess is, of course, 50% (although a recent paper that looked at 351k real coin flips says there’s actually a ~51% chance a coin tends to land on the same side it started on!), but there are gradients to wrong-ness that clearly don’t come out by just seeing how many times our guess was right.

For instance, the moment we ever get a coin flip that lands on tails, we can definitively say that Model 2 is instantly and completely discredited. It thought tails was literally impossible and failed to account for the fundamental randomness and noise in the process of a coin flip. Model 1 more correctly aligns with that fundamental randomness, but maybe not enough.

We need to measure both how often we’re correct and whether we are well calibrated to that fundamental randomness. Being correct as often as you think you ought to be correct is just as important as getting the actual heads/tails guess right. If you think a coin has a 51% chance of landing on heads, you’d want to observe yourself being correct guessing heads about 51% of the time. If you were correct in guessing heads 100% of the time, then your model wasn’t as confident as it could’ve been, despite it guessing correctly 100% of the time.

There are numerous statistical ways to evaluate this, but the important takeaway (while staying underneath my mathy-ness budget) is that writing an algorithm that is meant to accurately predict which team wins a match of pickleball is both about constructing a guess that, on average, is more right than wrong and simultaneously is trying to think about how confident its guess is in the first place.

How We Advance from Here

Ok, so we have an algorithmic starting place and we have a barometer of success, but how do we actually take the former and achieve the latter? Over the past few weeks, the analytics team has been able to rewrite and modernize our research pipeline from scratch under the sound principles that are required of a project being built for the scale and duration we envision for DUPR.

We created a full-fledged simulation engine with an ever-increasing list of model hyperparameters that we can now strategically and confidently tune through how they improve our success measurements, and have modularized our algorithm so that each individual component can be studied and improved specifically.

The hard work has been done to make the analytics workflow easy. Now the process is relatively straightforward:

1. Define a research project
2. Generate a model that fits into our engine and expose its hyperparameters to the simulator
3. Run the simulator over the grid of possible values
4. Pick the result that improves the predictive ability the most

Rinse. Repeat. Refine.

In the coming weeks, I’ll be continuing this series diving into how this has been applied at DUPR so far as well as poking around in some other fun curiosities. I hope you stay tuned and enjoy the behind the scenes look at DUPR’s analytical process!

For the most up-to-date information on how your rating is calculated, please visit our How It Works page.

Written By: Scott Mendelssohn - Head of Analytics, DUPR

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Here at DUPR, we're thrilled to unveil a groundbreaking feature that's set to revolutionize your pickleball experience – DUPR Feeds!

For the first time, we're introducing a dynamic way for players to connect, share, and engage within the vibrant pickleball community right from our app.

What's in Store with Feeds:

• Follow Friends: Stay in the loop with your pickleball pals! With the new Feeds feature, you can now follow your friends and catch up on their latest pickleball adventures, victories, and more.

• Post Text or Images to Feed: Share your pickleball journey with the community! Whether it's a stunning action shot or a post-game reflection, you can now post text or images directly to your feed for everyone to enjoy.

• Highlight Top Matches: Showcase your best plays and give fellow players a front-row seat to your pickleball progress.

• Comment on Players' Matches: Celebrate victories, share tips, or simply cheer on your fellow players. Feeds allows you to engage in lively discussions by commenting on the matches posted by others.

• Comment on Players' Posts: Connect with players through comments on their posts and keep the post-match buzz alive off the court.

• Tag Players: With Feeds, you can tag your friends and fellow players in your posts, ensuring they never miss a moment of the action.

• Tag Clubs and Events: Stay connected with the broader pickleball community. Tag your favorite clubs and events that you’re registered for in your posts to share the excitement.

Get ready to dive into an improved pickleball experience on the DUPR app with the new DUPR Feeds – the ultimate social hub for pickleball players.

While the web version of DUPR Feeds is coming next week, DUPR Feeds is available now on our mobile app!

Click here to learn more about other upcoming app updates.

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Disclaimer: This blog post may reference a previous version of the DUPR algorithm. Our system is constantly evolving to provide the most accurate and fair ratings possible. For the most up-to-date information on how your rating is calculated, please visit our How It Works page.

After months of research and gathering feedback from users, we are excited to announce that on January 30th, 2024, we will release key updates and improvements to the DUPR algorithm. We are confident these updates will create even more accuracy within our rating and a better experience for pickleball players everywhere.

Tomorrow, we're releasing a new blog post that provides a behind-the-scenes look at DUPR's research process and how it contributes to the continual advancement of the algorithm.

As of January 30th, 2024, DUPR takes into account:

1. Points Matter. Was this a tight match or a blowout? DUPR now evaluates the closeness of the match, so a score of 11-0 will have a bigger impact on your rating than a close match of 11-9 against that same opponent
2. Recency Matters. A match you played today will impact your rating more than a match you played six months ago.
3. Total Match Count Matters. If you've played in a lot of matches, your rating will fluctuate less compared to a player with a lower match count.
4. Every Match Matters (even against unrated players). Every match counts towards your rating, even if you are playing against someone who is unrated or new to DUPR.
   

FAQs

So what does the updated rating mean for you? We’ve tried to answer the most common questions from our community below.

Do these changes start now or will they be applied to all my past matches?

These updates will be applied across all your match results in our platform on January 30th, as well as to all future results you submit. Want to ensure your rating is accurate and up to date? Hit the court and share your results.

Why is DUPR updating the algorithm again? Is this going to happen frequently?

Our team of data scientists is dedicated to ensuring DUPR is the world’s best and most accurate global pickleball rating. As we get more players and results onto the platform, and have more data at our disposal, we are able to make improvements and updates to the system to ensure that DUPR is providing the most accurate assessment of level. We’re confident that each of these updates will improve the foundational accuracy of the rating. On 1/26/2024, we're releasing a blog post that provides a behind-the-scenes look at DUPR's research process and how it contributes to the continual advancement  of the algorithm. Will be linked here.

When do matches start to matter less? Is there a certain number of matches that count more (ie my most recent 5 matches)? When do matches start losing their value?

The weight of each match decays with each day that passes by. That means that any match played on the same day will have the same weight before considering the number of sets in the match. The match weight decays smoothly, so there is no cutoff for when a match is no longer counting.

What if all the players involved are not rated?

If all players are unrated, we do not know the level of the match, so we take a guess that the level is 3.5. The outcome of the match will push one team above 3.5 and the other below to properly reflect the levels displayed by the scores of the match. With each player’s next few matches, they will be able to move quickly toward their appropriate level, especially once they are playing rated players around their skill level.

Will my rating change if I haven’t logged in matches for a long period of time? I have 200 games entered but haven’t played in a year. How does that affect my rating?

A pause in playing will not directly affect your rating, but rather your reliability. In order to keep a reliable rating, we recommend that you continue to participate in DUPR logged or reported matches on a regular basis. If you jump back in after a break, you can expect some fluctuations in your rating in your first few matches back since your rating reached a low level of reliability. Once you record more matches, your rating will start to stabilize again and more accurately represent your current skill level.

Still have questions? Our support team is here to help. Fill out our new form: HERE

Seasoned Investors and Operators in Sports and Technology Join Effort to Unify Pickleball Through Technology and Universal Rating System

Austin, TX (January 24, 2024) – Today, DUPR (Dynamic Universal Pickleball Rating), pickleball’s most accurate international rating system, announced a new board, ownership and investors, as well as an influx of $8M in funding to further advance its mission to unify the fast-growing sport with innovative technology and a dynamic global rating.

David Kass, real estate developer and Major League Pickleball team owner, will operate as the new owner and chairman, alongside new board members and investors including legendary tennis star Andre Agassi; Raine Ventures, a wholly owned subsidiary of The Raine Group; Jay Farner, CEO of Ronin Capital Partners and former CEO of Rocket Companies; Brian Yeager, Chairman & CEO, The Champions Companies; and R. Blane Walter, founder of InChord Communications. Tito Machado will continue to lead the organization as its CEO.

“Pickleball is still in its infancy and the sport is only going to continue to gain traction in the US and worldwide. We are making a major financial investment in DUPR to grow the platform, its technology and the game for the long term,” said David Kass, DUPR Chairman of the Board. “In just a few years, DUPR has experienced incredible adoption, with over 35 API partners and half a million users, enabling players to know their level, connect with friends and players, join clubs, and unlock advanced analytics, training tools and opportunities for upskilling. Now operating as its own entity, DUPR can focus on cementing its position as the connective tissue of the entire pickleball ecosystem. I am excited to work with this talented team to take DUPR into its next chapter.”

With over half a million users and a 20% month over month growth rate since its founding in 2021, and a presence in over 140 countries, including China, Germany and the UK, DUPR has established itself as the global standard in pickleball ratings. DUPR provides players across all levels with an ageless, genderless rating based on their match results to equip the emerging sport with a simple language and standard to gauge player ability.

“As an active, passionate pickleball player I know how important it is to play against players  at your level for the ultimate enjoyment of the sport,” said Andre Agassi, former world #1 tennis player, eight-time Grand Slam champion and Olympic gold medalist. “DUPR is the most accurate system out there that gives all players a simple and easy way to know their skill level. With DUPR, you can also track progress, connect with others and maximize more opportunities to hit the courts. I believe in DUPR’s vision to benefit pickleball and players of every level. I am excited and proud to be a part of this team.”

DUPR’s various organized competition models provide endless opportunities to participate in pickleball. From juniors (focused on shaping young talent that will define the future of the sport), to collegiate (DUPR is the home of collegiate pickleball), all the way to Major League Pickleball — DUPR connects the entire pickleball pathway, offering something for everyone. The company’s partners and supporters include LifeTime Pickleball, The Picklr, Ace and OOFOS.

“Our team has been working tirelessly for the last few years to build the best-in-class rating system and technology platform to unify pickleball. Now, with the support and guidance of David Kass, Andre Agassi, The Raine Group and our other incredible investors and board members, we’re well-positioned to accelerate our growth and adoption worldwide, while also releasing new features to the platform to enhance the experience for players and organizers,” said Tito Machado, DUPR CEO.

DUPR uses a modified Elo algorithm that rates players, regardless of their age, gender, location, or skill, across the same 2.0 to 8.0 scale to evaluate their ability. The algorithm considers three factors: wins or losses, type of match (recreational vs tournament) and the rating spread between players. DUPR is free and one match is all it takes to have a rating. As players play more, their rating becomes even more accurate. Players who use DUPR play more often, and love the ability to know their level and find more opportunities to play. Tournament directors and event organizers report that when their players have a DUPR they see more activity at their club and increased growth for their bottom line.

To learn more about DUPR, please visit www.mydupr.com.

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About DUPR 

DUPR (Dynamic Universal Pickleball Rating) is the premier global pickleball rating system and technology platform, trusted by the world’s leading clubs, tournaments, leagues and players. DUPR’s dynamic rating system unifies pickleball across age, gender and location by analyzing match results to accurately evaluate all players across a 2.000 - 8.000 scale. Players and operators can visit  www.mydupr.com to sign up and learn more.  

Media Contact

Erica Annon / DUPR / erica@mydupr.com 

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Connor Garnett, a Professional Pickleball Player, was introduced to pickleball through his first Minor League Pickleball event, which instantly sparked his passion for the sport. With a background in college tennis and investment banking, his competitive and fearless nature led him to pursue professional pickleball at the highest level in the country. During the interview, he discusses his experience in his initial Minor League event, his journey to the Major Leagues, and his personal views on the DUPR rating system.

 How did you first hear about Minor League Pickleball and what made you try it?

“I was still working full time at that point and just getting into pickleball and a couple of buddies were forming a team and they were like, “Do you want to come out this weekend to play this tournament?” It'll be a fun team event. Having come from college tennis, it instantly was appealing in that regard and it was awesome. I also didn't have to give up any work at that time. So it's kind of playing that balancing act where the regular tournament started Thursday. This was a perfect opportunity to get out there play a tournament and meet a ton of people with a few good buddies. So I was stoked to get out there and play with them.” 

 Any standout moments or experiences from your first time at Minor League?

"Having that scoring (MLPlay) there puts a little more pressure. It got me hooked a little bit more on pickleball coming from not really competing the last couple of years and just working in investment banking. I was able to get out there and have that Collegiate environment, which was a ton of fun. I remember us just battling it out. There was pool play which was also fun. You just got to see a lot of different teams and then you ended up with semis and finals which was a super cool way to do it.”

Do you think playing Minor League Pickleball prepared you to play Major League Pickleball? Was there a smooth transition? Any adjustments, both in your game and mentally?

"Yes. I think the level of errors goes down and so in Minor League, you get used to the format and the style of it, but you can still get away with a lot of stuff that's not going to work in Challenger (Major League Pickleball).  Each level has its challenges, but Minor League helped me build confidence and get used to the format. The jump was significant, especially in the first Challenger event. The pressure increased, and you have to fully trust your game. Facing top players in premier level MLP means every match is a battle out there, so it's just getting used to those levels and putting in the work on the reps so you can fully trust your game."

Who would you recommend Minor League for, and why?

"Minors is great for those entering the sport and wanting their first exposure, or just looking to have a good time. It got my foot in the door and got some buzz around my name but it's also fun for those players who are just looking to have a good time. The Minor League events are out there, and it's a cool way to make a little bit of money and play the sport that you love. Like the one I attended, it added a fun twist with team costumes where the winner of the contest received money. It's not just competitive;  it's also a place where you can go and hang out after the matches and just talk to everyone. It's a great stepping stone, but then you want to be focusing on the PPA tour stops to make that jump."

What are your thoughts on pickleball ratings and accuracy overall?

"DUPR does a good job of collecting more data, especially for lower levels. I know when I was first getting into the sport I had to call and ask about getting my ranking properly adjusted so I could play pro tournaments and it just wasn't picking up certain things and wasn't as Dynamic. I think DUPR has a great kind of functionality on capturing those earlier players and putting a ranking on them to at least help them make sure they're in an event but I think sandbagging is always gonna be prevalent. No matter how you slice it. It's just trying to mitigate it as much as possible."

How important is having an accurate rating for the sport, and should there be one standardized system?

"I don't look at ratings much but being able to accurately get that having one system out there that can just put a value on everyone. It is so important and I think that's just going to be crucial. The more systems you have the more confusing it gets so it's just the more you can standardize that the better it's gonna be."

Lastly, any feedback for DUPR 's improvement?

"I love the DUPR system. I think it's great for some of the waterfall events and just being able to have that flexibility in these different formats is awesome. I think the cool thing about DUPR is you have been constantly tinkering with the system, experimenting with it. I like that because it's a learning process, figuring out what makes sense from my perspective. I'm not as close to that stuff. I think, for the pro level, DUPR does a cool job showing probabilities."

Follow Connor's pickleball journey on Instagram.

View the full interview on our YouTube channel.

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