Whether you first stepped on the court several years ago or several days ago, you’ve probably heard pickleball players talking about their ratings.

“I’m a 3.5, but I’m trying to get to 4.0 by the end of the year.”

“I thought I was a 4.0, but I just played in a tournament against other 4.0 players and they totally destroyed us. I think we were sandbagged.”

“You’re a 4.5? I’m only a 3.0. No way am I playing against you.”

Player ratings help us gauge our skill level compared to our competitors, find good match-ups with other teams, and measure our improvement over time. That last piece is probably the most important–without ratings, you have no way to determine how much you’ve improved or how much more work you need to put in to reach your goals.

That being said, how you’re rated as a player depends on your rating system–and not all of them are created equal. Read on for an introduction to the different types of rating systems, plus the pros and cons of each.

DUPR (Dynamic Universal Pickleball Rating)

📸 by DUPR 

DUPR is one of the most popular pickleball rating systems, especially for amateur players. It includes both doubles and singles play and is designed to be a universal standard.

Algorithm: DUPR includes both doubles and singles, and is based on your most recent 60 matches (for doubles) or 30 matches (for singles) within 12 months. It employs a proprietary algorithm, which considers factors like the competitive level of the match, the type of match (club, tournament, etc.), and winning/losing to provide a rating between 2.0 and 8.0.

Pros:

• Incorporates both sanctioned and non-sanctioned matches.

• Universal approach, aiming for a standard that can be applied anywhere.

• Considers a broad history of recent play, not just tournaments; meaning you can have a DUPR score even if you don’t play in tournaments.

Cons:

• Some players believe it can be slow to respond to skill changes.

• It can be biased based on region because the algorithm factors in the skill level of those you play with; i.e. a 4.5 player in California might be very different from a 4.5 player in Florida.

• It does not currently factor in point differential or margin of victory, only wins and losses. Your DUPR can go up only if you win, and will go down if you lose–even if the match was close against more highly rated opponents.

• Algorithm updates happen often, and aren’t always clearly communicated, which can be confusing for players looking for a consistent rating system and can cause ratings to shift even if the data doesn’t change.

USA Pickleball Association (USAPA) Player Ratings‍

A player’s USA Pickleball Tournament Player Rating (UTPR) is calculated based on the player’s performance in USAPA-sanctioned tournaments. The rating algorithm takes into account tournament wins/losses as well as the ratings of all opponents. Ratings are initially calculated as 4-digits, then rounded down to provide a 2-digit rating ranging from 1.0 to 6.0+.

Players without a UTPR can self-rate to enter tournaments. Guidelines are provided by USAPA to help players self-rate, by assessing their proficiency in skills such as dinking, serving, volleying, and strategizing.

For example, a player just starting out in the game without much sports experience would be a 1.0-2.0 player, whereas a pickleball pro would rank at 5.0 and above.

Algorithm: The UTPR calculates a player’s 4-digit rating for each player, adjusting after each sanctioned tournament based on match outcomes and the ratings of opponents. That 4-digit rating is then rounded down to a 2-digit skill rating.

Pros:

• Based on actual match results, which can be a strong indicator of performance.

• Encourages competitive play in sanctioned tournaments.

• The 4-digit UTPR is calculated on a weekly basis.

Cons:

• May not accurately reflect the skill of players who don't frequently compete in tournaments.

• Because players can use self-ratings to enter tournaments (at least at first), they can ‘play down’ against players of lesser skill and thus enhance their UTPR unfairly.

• UTPR may be slow to reflect changes in skill. For example, if you’re rated at a 3.5, but practice has brought you closer to a 4.5 skill level, you can still play in a 3.5 tournament–but winning all of those matches at 3.5 will not have much of an effect on your rating.

• UTPR could be biased based on age or region if players do not participate in a variety of age categories and localities.

Pickleball Tournament Rating (WPR)

PickleballTournaments.com developed the World Pickleball Ratings (WPR) system to be as comprehensive as possible.

Algorithm: The WPR algorithm uses a Glicko-2 ratings system to calculate a player’s rating on a quarterly basis based on factors such as results from sanctioned and non-sanctioned tournaments, match frequency, and opponents’ player ratings.

Pros:

• Tailored for tournament players.

• Decreases ‘sandbagging’ in tournament play by reducing the use of self-rating for brackets and seeding.

Cons:

• Like UTPR, it may not accurately reflect the abilities of players who do not compete in tournaments, such as those run through PickleballTournaments.com.

• The proprietary nature of the algorithm means players can't precisely predict how their actions will affect their rating.

Less Common Rating Systems

There are a handful of additional rating systems, but they are less common:

• World Pickleball Federation (WPF) Rating System: WPF uses a system similar to DUPR but is used by the WPF for international play. Players can view their rankings at global, regional, and country levels, and that ranking takes into account the best twelve results over the past year. The pros and cons for this rating system are similar to DUPR’s.

• International Pickleball Federation (IPF) Rating System: Similar to USAPA's rating, providing guidelines for international competition levels. The pros and cons of IPF rating are similar to USAPA’s.

• Local Club or Regional Ratings: Some local clubs have their own rating systems based on local league play or assessments. These ratings may not always translate well to national or international levels but are used for internal events and ladder play.

Remember, each rating system has its specific goals and use cases, and the best way to improve your rating will depend on your competitive focus and the resources you have available, including using advanced analytics platforms like PB Vision to get more detailed insights into your game. 

With recorded data from everything that happens on the court, PB Vision is working toward a more objective rating system. For more information about how computer vision can be used to improve your game and–one day–provide you with the most accurate player rating, read on.

Finding flaws in existing pickleball skill rating systems doesn’t require much digging. Although they can be useful, DUPR, UTPR, WPR, and others rely on some level of subjectivity, leading to difficulty in accurately assessing player skills. This is where the innovative application of computer vision technology comes into play, heralding a shift towards a more objective and accurate evaluation of player abilities.

What is Computer Vision?

Computer vision is under the umbrella of artificial intelligence (AI), and it enables computers to interpret and make sense of the visual world. By processing digital images, videos, and other inputs, computer vision systems use deep learning algorithms to identify, classify, and respond to various elements within these visuals. The implications of this technology in sports analytics are significant–many sports have already benefited from the application of computer vision. Balltime provides AI-generated stats for volleyball, Trackman helps improve golf performance, SIQ helps train basketball players, Team Mustard analyzes skills for baseball and football.

Consider a technology that can scrutinize every moment of a pickleball match, where every shot, player movement, and tactical decision is captured and evaluated from video footage. Computer vision analysis involves breaking the footage into individual frames and applying sophisticated algorithms to extract meaningful data from these images. The result is a rich, multi-dimensional understanding of the game, distilled into actionable insights. 

Capturing Data for Analysis

To effectively access PB Vision’s computer vision analysis, you first need a high-quality video recording of your match. Ensuring the right framing and resolution is crucial for accurate data extraction. For tips on capturing the best footage, refer to our best recording practices article.

Analyzing the Data: Game Insights

The backbone of PB Vision is its sophisticated algorithm, which employs machine learning and computer vision to meticulously analyze videos of pickleball matches. It assesses various aspects of player performance, including shot accuracy, types of shots, player movement, and error rates (see below). This analysis is then transformed into precise data models, offering unprecedented insight into a player’s performance.

Once the video is processed through PB Vision's system, it yields a wealth of insights. Players can explore detailed visualizations of their performance, such as heatmaps (see below) showing shot placements, analysis of player movement patterns, and breakdowns of error rates. These insights go beyond mere statistics, offering a deeper understanding of a player's strengths, weaknesses, and areas for improvement.

For instance, a heatmap may reveal a player’s tendency to favor one side of the court, or a particular stroke analysis might highlight a recurring technical flaw. Such detailed feedback is invaluable for targeted skill development.

Translating Computer Vision Analysis to Player Ratings

While PB Vision does not currently assess player ratings, it lays the groundwork for a new kind of rating system based on objective game insights.

Imagine a rating system where your score directly reflects specific, measurable aspects of your game, analyzed by advanced AI, namely large language models (LLMs). These sophisticated AI systems, renowned for their ability to understand and generate human-like text, could take our objective analysis of player performance to new heights. By integrating LLMs, PB Vision could achieve a higher abstraction of insight, extracting nuanced interpretations from the raw data. This system could analyze physical gameplay and provide strategic advice, mental game coaching, and personalized feedback by "reading" more into the data than we currently comprehend. This integration could transform how we understand and improve in pickleball.

A more objective rating system based on these insights could go beyond a simple decimal like ‘4.0’ or ‘3.5’--players could have a more detailed breakdown of their ratings in categories such as speed, backhand skill, maximum forehand drive speed, spin ability, shot decision making, reset capability, and serve quality. Instead of relying on tournament outcomes or subjective evaluations, this system would consider the intricacies of a player’s performance. No longer would a player’s rating be dependent solely on wins and losses or a partner’s skill level– ratings would be as accurate a reflection of a player’s true ability as possible.

Assessing a Computer Vision-Based Rating System

Pros

• Comprehensive Skill Analysis: Provides a layered and more nuanced view of player abilities, encompassing technical, tactical, and physical aspects.

• Dynamic Performance Tracking: Allows players to monitor their development over time, adapting to different game situations and strategies.

• Inclusivity: Offers valuable insights for players at all levels, regardless of their participation in competitive play.

• User-Centric Adaptive Approach: Adapts to individual player needs and aspirations, such as strategies, playing tempos, and preferred shot types.

• Objective Evaluation: Significantly reduces biases related to external factors, offering a more balanced and fair assessment of skills.

• Privacy Protection: Does not store biometric data to best protect player privacy.

Cons

• Potential Inaccuracies: Computer vision is still error-prone and will never be 100% accurate.

• Reliance on Recording Quality: The error margin largely depends on recording quality.

• Essential Need for Player Tagging: Players must be manually tagged across games.

Improving with an Objective System

To make the most of a computer vision-based rating system like PB Vision, players should:

• Focus on Targeted Feedback: Use the detailed analysis to zero in on specific weaknesses or areas needing improvement.

• Embrace Consistent Evaluation: Regularly upload gameplay videos to take advantage of performance tracking over time.

• Engage Actively with Tools: Utilize the platform's various tools and training recommendations to systematically refine your skills and strategies.

The integration of computer vision technology in assessing pickleball player ratings opens up a new world of sports analytics. With the potential to offer objective, detailed, and actionable insights, this technology can revolutionize the way players understand and improve their game. As we continue to develop and refine these tools, the future of pickleball ratings looks more precise, inclusive, and data-driven, enabling players at all levels to elevate their game based on clear, objective metrics. Stay tuned to PB Vision as we lead the charge in this exciting new frontier of pickleball analytics.

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