Statistics on a Blockbreaker-like Game

The author is in the process of creating a blockbreaker-like game, in which the jumping-off point is the "Block Breaker" section of the Udemy course, Complete C# Unity Developer 2D: Learn to Code Making Games

After making lots of levels, the author needed to sort them by difficulty. How does one measure the difficulty of a level? A first-cut solution is to make an auto-play bot that is not perfect, and see how well the bot does on each level, using thousands of trials.

Here is a video of the game in auto-play action.

Fields

• Date: date and time the game was auto-played
• Level: the name of the level (the 3-digit number is an estimate of the difficulty from a previous run, no longer valid after tweaking)
• NumBlocks: how many blocks have to be broken to win the level
• IsWin: True if autoplay broke all the blocks, False if the ball fell past the paddle.
• ElapsedTime: Seconds until either won or lost (game is played at 4x speed, so multiply by 4 to get an estimate of how long a human might play it)
• Score: total score when the game was won or lost
• Accuracy: the autoplay is tuned with a randomly-chosen accuracy. Higher numbers are more likely to win.

Introduction

Board Game Statistics: ​Board games have experienced a notable resurgence, becoming a preferred pastime for diverse demographics. In the United States, 43% of board game enthusiasts engage in gameplay several times per week, while 25% play weekly. Demographically, 47% of board game players are aged between 18 and 34, with individuals aged 55 and above comprising 18% of the player base. Gender representation is balanced, with women accounting for 51% and men 49% of board game enthusiasts. Regarding game collections, 43% of gamers own more than 25 board and/or card games.

The average playing time for most board games ranges from one to two hours, though this can vary significantly depending on the game's complexity. These statistics underscore the enduring appeal and diverse engagement within the board gaming community.​

Context

A team's mean seasons statistics can be used as predictors for their performance in future games. However, these statistics gain additional meaning when placed in the context of their opponents' (and opponents' opponents') performance. This dataset provides this context for each team. Furthermore, predicting games based on post-season stats causes data leakage, which from experience can be significant in this context (15-20% loss in accuracy). Thus, this dataset provides each of these statistics prior to each game of the regular season, preventing any source of data leakage.

Content

All data is derived from the March Madness competition data. Each original column was renamed to "A" and "B" instead of "W" and "L," and the mirrored to represent both orderings of opponents. Each team's mean stats are computed (both their stats, and the mean "allowed" or "forced" statistics by their opponents). To compute the mean opponents' stats, we analyze the games played by each opponent (excluding games played against the team in question), and compute the mean statistics for those games. We then compute the mean of these mean statistics, weighted by the number of times the team in question played each opponent. The opponents' opponent's stats are computed as a weighted average of the opponents' average. This results in statistics similar to those used to compute strength of schedule or RPI, just that they go beyond win percentages (See: https://en.wikipedia.org/wiki/Rating_percentage_index)

The per game statistics are computed by pretending we don't have any of the data on or after the day in question.

Next Steps

Currently, the data isn't computed particularly efficiently. Computing the per game averages for every day of the season is necessary to compute fully accurate opponents' opponents' average, but takes about 90 minutes to obtain. It is probably possible to parallelize this, and the per-game averages involve a lot of repeated computation (basically computing the final averages over and over again for each day). Speeding this up will make it more convenient to make changes to the dataset.

I would like to transform these statistics to be per-possession, add shooting percentages, pace, and number of games played (to give an idea of the amount uncertainty that exists in the per-game averages). Some of these can be approximated with the given data (but the results won't be exact), while others will need to be computed from scratch.

Comprehensive NFL game statistics and predictions for Los Angeles Rams at Cincinnati Bengals, including scores, spreads, over/under, and advanced analytics.

As a massive League of Legends fan for 10+ years, I realized that there weren't any datasets that helped us stay updated with Worlds 2021, thus this dataset was born!

All data was acquired from lolesports.com which shows all in-depth statistics available for each match that others can use to find correlations between in-game statistics and wins.

I would love to see this data used to answer how vision (ward interactions) and gold distribution (how a team's gold is divided among it's positions) correlate with win percentage.

Comprehensive NFL game statistics and predictions for New York Giants at San Francisco 49ers, including scores, spreads, over/under, and advanced analytics.

As the name suggests, free-to-play video games give players access to content free of charge, although sometimes payment is required to enhance in-game features. The global free-to-play mobile gaming market is estimated to reach ***** billion U.S. dollars in 2024, up from ***** billion U.S. dollars in 2023.

After overtaking video game purchases, in-game consumer spending accounts for the biggest share of the video gaming market. In 2020, global gaming audiences spent an approximate ** billion U.S. dollars on additional in-game content. In 2025, the market value of in-game purchases is projected to surpass **** billion U.S. dollars.

Released in April 2025, Clair Obscur: Expedition 33 was the highest rated video game of the year based on aggregate critic score. The role-playing video game was developed by Sandfall Interactive and published by Kepler Interactive and generated a Metascore of 92. In second place, also with a Metascore of 94, was puzzle adventure Blue Prince.

NBA Player Game Stats - 2024/2025 Season

This dataset provides comprehensive performance statistics for NBA players throughout the 2024/2025 season. It includes both advanced and traditional stats, making it ideal for player performance analysis, efficiency assessments, and exploring game patterns and trends. Data was collected from reliable sources, ensuring quality and consistency across each record.

Column Descriptions

• Player: Name of the player.
• Tm: Abbreviation of the player's team.
• Opp: Abbreviation of the opposing team.
• Res: Result of the game for the player's team.
• MP: Minutes played, represented as a float (e.g., 23.5 = 23 minutes and 30 seconds).
• FG: Field goals made.
• FGA: Field goal attempts.
• FG%: Field goal percentage.
• 3P: 3-point field goals made.
• 3PA: 3-point field goal attempts.
• 3P%: 3-point shooting percentage.
• FT: Free throws made.
• FTA: Free throw attempts.
• FT%: Free throw percentage.
• ORB: Offensive rebounds.
• DRB: Defensive rebounds.
• TRB: Total rebounds.
• AST: Assists.
• STL: Steals.
• BLK: Blocks.
• TOV: Turnovers.
• PF: Personal fouls.
• PTS: Total points scored.
• GmSc: Game Score, a metric summarizing player performance for the game.
• Data: Date of the game in YYYY-MM-DD format.

Usage Examples

This dataset is perfectly suited for: - Statistical analysis: Gain insights into player and team performance trends. - Machine learning projects: Build predictive models using detailed player stats. - Performance prediction: Forecast player outcomes or team results. - Player comparisons: Analyze players across various metrics and categories. - Efficiency analysis: Evaluate player and team efficiency, comparing statistics across games. - Game trend exploration: Investigate patterns within the season, identifying shifts in strategies and performance.

The Tabletop Role-Playing Game (TTRPG) market has evolved dramatically over the past few decades, emerging as a vibrant community of storytellers and strategists engaging in immersive gameplay experiences. Initially characterized by traditional pen-and-paper formats, the industry has diversified to include a plethor

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Analysis of ‘NBA 2020-21 Regular Season Player Stats Per Game’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zgrbalbay/202021-reg-tra-player on 13 February 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Introduction

Gaming Monetization Statistics: The gaming sector has undergone considerable transformation, moving away from its conventional model of selling games as independent units.

Instead, it has embraced a diverse ecosystem where various strategies are utilized to produce revenue. Gaming monetization pertains to the practices and tactics employed by game developers and publishers to derive earnings from their creations.

This multifaceted methodology holds immense importance in maintaining the industry's expansion, facilitating game development, and furnishing players with captivating interactions. The dynamic gaming monetization landscape is characterized by its ability to adapt to changing market dynamics, emerging player preferences, and technological advancements.

This adaptability will remain essential in sustaining the industry's growth while ensuring that players continue to receive captivating gaming experiences.

Comprehensive NFL game statistics and predictions for New England Patriots at Dallas Cowboys, including scores, spreads, over/under, and advanced analytics.

An October 2023 survey of gamers in the United States found that arcade and puzzle games were among the most popular video gaming genres across all generations. About 64 percent of Gen Alpha gamers stated arcade games as one of their top three gaming categories.

Context

I used the Paul Rossotti’s data set on my personal projects. However, after a long time using it I noticed that I would need more old and recent data, so I ended up with a more complete data set and I thought that might help someone. Since his data set was used as a base, all the credits goes to him. I only incremented it. Also I am willing to update this data set yearly.

You can access his work using this link on the reference section.

Content

This data set contains information about the box score of every NBA game since 1949-50 until now. You can get the data individually for each season, decade or a compiled of all the data. In total the data set has, approximately, 120 features/columns/attributes that goes from basic stats (like total points, rebounds, assists, blocks, and so on) to more advanced ones (like floor impact counter, assist rate, possessions, pace, play% and much more!).

Each game will contain the same features to the home team and its opponent (away team) and some other features related to the game itself (like game date, season, season type and match winner). If you like stats and NBA, this data set was made for you!

If do you wanna more about the formulas used and its meaning, please check the reference section. Also you can check the “features_description” file. There you will find a brief description of each feature and its respective formula (only for more advanced stats).

Acknowledgements

LAST TIME THE DATA SET WAS UPDATED:

July 26, 2021 (07/26/2021) – 1pm EDT

Questions about the dataset:

Q:How did you collected the data? A: I created a web scrapper using python to do the hard work.

Q: How did you filled the missing values? A: For the float columns I filled with “0.0”. For the object columns I left with a NaN value, but don’t need to worry about it. The only columns that I need to do that was teamWins, teamLosses, opptWins, opptLosses. However only 8 rows in the entire data set has NaN values! Great news, isn’t it?

Q: Where can I see the description/formula for each attribute/column/feature? A: You can check it out in the “features_informations” file inside the data set.

Q: Will you constantly update the data set? A: Yes!

Q: The data contains only regular reason games? A: No! The data contains playoffs games as well.

References

About the stats and formulas used: https://www.basketball-reference.com/about/glossary.html https://basketball.realgm.com/info/glossary https://www.kaggle.com/pablote/nba-enhanced-stats (Paul Rossotti’s data set)

Where the data was collected: https://www.basketball-reference.com/leagues/

The difference represents . Significance codes: ***: p < .001, **: p < .01, *: p < .05. The home team advantage is also presented.

Dataset

This dataset was created by Bill Basener

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