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For most kids, gym class is the highlight of the school week. It’s a much-needed break from serious class work and a chance to run around, play with friends, and expend energy playing basketball, soccer, volleyball, and floor hockey. Today, an increasing number of schools are adding pickleball to the menu as a way to provide students with a fun, easy way to increase physical activity and combat childhood obesity while addressing their ever-shrinking budgets.

Tailor-Made For Schools

Many aspects that have made pickleball so popular with adults – it’s easy to learn, it’s a fun workout, games are quick, and it allows people of various abilities and fitness levels to play together – also make it a great fit for school gym classes. Students can progress very quickly, which builds up their self-esteem and a sense of accomplishment and can lead to improvements in their overall academic achievement.

Pickleball is also ideal for schools because it solves the problem of how to provide students with a fun physical activity while adhering to tight budgets. According to The Society of Health and Physical Educators, (SHAPE America), the average elementary school spends only $462 per year on physical education. That’s not per child —that’s for the entire school. Given these constraints, pickleball provides a cost-effective activity that many students can play while meeting government mandates for physical education.

DUPR Fuels the Growth of Pickleball Among Younger Players 

The Sports & Fitness Industry Association (SFIA) estimates that 8.9 million people played pickleball in 2023. As the average age of pickleball participants continues to trend downward – it’s currently at 35 – DUPR is working with organizations across the country to expand the sport further among younger players. For example, the most recent National Junior Pickleball Championships powered by DUPR was held in October in Las Vegas and featured 113 boys and girls ranging in age from 9-18 competing in singles, doubles, and team tournaments. 

Bringing Pickleball to Schools

In addition to DUPR’s efforts, the nonprofit organization PHIT America is making it easy for schools to add pickleball to their physical education curriculum. PHIT America’s Play Pickleball initiative donates everything a school needs – paddles, nets, balls, training aids -- to introduce pickleball to students. Launched in Spring 2023 with the ambitious goal of delivering more than 100 kits to elementary schools throughout the US, PHIT America is partnering with pickleball pro KaSandra Gerke to spread the word about the fun and health benefits kids can enjoy from playing pickleball.

Pickleball is all about teamwork and good sportsmanship and therefore fits nicely with the values schools teach every day. The sport’s explosive growth over the past few years is poised to continue, and anyone who has watched an MLP or PPA match is witnessing the success younger players are having on tour. Seventeen-year-old Anna Leigh Waters continues to dominate and teen phenoms Hayden Patriquin and Quang Duong consistently compete at the highest levels. You never know, somewhere in America a student could be picking up a paddle in gym class for the first time, get hooked on the sport and soon play in a few pickleball tournaments, get their DUPR rating, and maybe become tomorrow’s next great pickleball champion.

‍Professionals Can Access Industry-Leading Tools and Technology to Successfully Introduce and Grow the World’s Fastest-Growing Sport in Their Communities

DUPR (Dynamic Universal Pickleball Rating) and The Registry of Pickleball Professionals (RRPk), the pickleball arm of the International Coaches Institute, today announced a long-term partnership to support the growth and development of club owners and coaching professionals with pickleball education training and resources. 

Pickleball is the fastest-growing sport in the US and is seeing explosive growth worldwide. DUPR and RPPk are collaborating to launch an educational training program and platform for professional coaches and event organizers to equip them with tools and training to capitalize on the excitement around pickleball and successfully introduce the sport in their communities. DUPR’s accurate global rating system and technology platform provide a unifying language and standard to evaluate player skill level and find opportunities to play, creating connectivity for the sport around the world. 

“Our mission is to serve our members with the best tools and training to enhance their professional development. Pickleball is only continuing to grow worldwide and our partnership with DUPR will give our professionals the resources they need to effectively introduce and grow the sport in their communities,” said Luis Mediero, President of the Registro Profesional de Pickleball and International Registry of Pickleball Professionals. “We specialize in coaches and education, and DUPR specializes in the best, most accurate pickleball ratings and technology. It’s a natural fit for our organizations to come together to support our professionals and grow the sport effectively worldwide.”

RPPk will provide a DUPR Team Coach qualification to all coaches who pass the corresponding training courses in their professional training program, and will release a number of certification programs to support organizers in their professional development later this year. The partnership will kick off with a pickleball tournament in Madrid, where coaches will be able to take the first RRPk course and experience the power and precision of DUPR during a championship tournament.

“Professionals are the lifeblood of the game, and we are committed to providing the training, tools and technology to grow pickleball and make the experience fun and exciting so players keep coming back to the court,” said Tito Machado, DUPR CEO. “Our focus at DUPR is to bring accessible, easy to understand technology and analytics to pickleball to unify the sport across age, gender and location with a simple rating system. We are honored to partner with the RPPk and the International Coaches Institute to support professionals and the growth of the game worldwide.” 

RRPk and DUPR are developing the following programs to be released later this year: 

• Players’ Rating Program: This will train certified RPPK International Professional coaches in establishing initial DUPR ratings for new players to evaluate player skill level, organize level-based play and start to get results into the system. 

• Tournament Director Course: Coaches will learn how to use the DUPR platform to develop and organize successful pickleball events and tournaments. 

• Team Manager Course: Team organizers will learn how to use the DUPR platform and successfully direct and captain a team in a pickleball league.

• Referee Certification Course: Open to licensed International Referee Professionals, this course will teach how to use the DUPR platform and the necessary regulations to referee a pickleball tournament. 

• Professional Services: RPPk offers a series of professional services so that all professionals certified through the RPPk & DUPR training and certification programs can carry out their work with total safety and guarantee.

For more information, please visit www.RPPickleball.org

About International Coaches Institute & RPPk

The International Coaches Institute - ICI established during 2021, the Registry of Pickleball Professionals - RPPk dedicated to professional training, international certification and comprehensive professional services for PICKLEBALL teaching professionals, as well as the promotion of this sport. RPPk has more 1,200certified coaches in 21 countries, who have the highest recognition in the Pickleball industry. All our qualifications are recognized by: International Registry of Pickleball Professionals (RPPk International), European Registry of Pickleball Professionals (RPPk Europe), United States Registry of Pickleball Professionals (RPPk USA), Registro Profesional de Pickleball Latinoamericano (RPPk Latinoamérica), International Coaches Institute (ICI) and the y Dynamic Universal Pickleball Rating (DUPR). 

All training programs, certification and qualifications and professional services of the Registry of Pickleball Professionals - RPPk are endorsed and recognized by the Royal Spanish Tennis Federation - RFET / PICKLEBALL and by the Spanish Pickleball Association - Pickleball Spain.

The International Coaches Institute (ICI) has become the first and largest international organization dedicated to offering educational programs, international certification and comprehensive professional services to tennis, golf, padel, fitness and pickleball teaching professionals through their respective Professional Registries: Tennis (RPTennis), Golf (RPGolf), Padel (RPPadel), Fitness (RPFitness) and Pickleball (RPPk) with a clear objective: to offer all those tools necessary to train and develop people through sport. 

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.

Sign Up For Free DUPR Account

Tyson McGuffin is one of the most well-known names in pickleball. As one of the top players in the sport, he has made a significant impact with his skills and dedication to the game. In this Q&A session, we get to know Tyson, who is a DUPR athlete and active member of our community, a little bit better. We discuss his favorite hobbies, goals, and his thoughts on the DUPR Rating System.

What is your favorite hobby (jokingly outside of pickleball of course)?

“ I just picked up golf and am really loving that. Also cooking. I love my wife, but I do all the cooking.”

What is your favorite destination that you have played pickleball in?

“I love Palm Springs. Weather is nice, golf is great, and the vibes are good.”

What are some goals you have set for yourself in 2024 - whether personally or professionally in pickleball?

“I am determined to keep working on my craft, always stay grounded and be the people's champ. No matter what category - husband, father, player, coach, content creator - I want to shoot for 100%.”

How do you think the DUPR Rating System has enhanced the overall pickleball experience? Do you often or ever compare your DUPR to other players that you are playing, is it a factor going into a match that you play?

“Having an accurate rating system helps the level of play. It creates consistency and sustainability in the sport. It ensures equal play in a competitive setting. The algorithm helps determine how to organize play when you have a range of ratings. Knowing everyone’s DUPR helps keep matches competitive when levels are all over the place. 

What inspired you to go pro?

“I was done playing tennis, but still wanted to compete. I was turned on to this silly sport by a member at the club where I was teaching. I have zero moderation in most things in life so dove fully into going pro quickly.”

We are proud to call Tyson McGuffin a DUPR Athlete. His dedication and love for the sport is obvious. With his goals set for continual growth and providing an inclusive playing environment through DUPR, Tyson is making his mark on the pickleball world and inspiring others to do the same.

Make sure to stay updated on Tyson McGuffin's ongoing journey as a DUPR athlete in 2024. Follow him on Instagram and tune into his podcast to catch all of Tyson's upcoming appearances.

Follow Tyson and all your favorite DUPR Athlete's on the DUPR app - Download now!

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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

###

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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