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Skill-based matchmaking
Skill-based matchmaking
Skill-based matchmaking
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Skill-based matchmaking (SBMM), also referred to as matchmaking ranking (MMR), is a form of matchmaking dependent on the relative skill level of the players involved.

History

[edit]

A common rating system in chess is the Elo rating system, developed by Arpad Elo. Former International Chess Federation president Florencio Campomanes described it as an "inseparable partner to high-level chess".[1] In 2006, Microsoft researchers proposed a skill-based rating system using Bayesian inference and deployed it on the Xbox Live network, then one of the largest deployments of a Bayesian inference algorithm.[2] The researchers were displeased with the ranking system in the beta of Halo 2 (2004).[3] By the time Halo 2 launched, it was using TrueSkill.[4]

The term skill-based matchmaking first appeared in a 2008 interview with game designer John Carmack in which he emphasized its importance in Quake Live (2010). Upon setting up an account with id Software, the game will ask the player for their skill level and judge accordingly depending on their performance from that point forward.[5] The presence—or lack thereof—of skill-based matchmaking became a point of contention. During the development of Dota 2 (2013), Valve Software believed that the barrier to entry could be solved with, among other things, skill-based matchmaking through its Steamworks service;[6] when Call of Duty: Black Ops (2010) developer Treyarch was asked why the game wouldn't include skill-based matchmaking unlike Halo 3 (2007), multiplayer design director David Vonderhaar said that speed was "more important than anything else".[7]

Description

[edit]

Team-based, competitive games such as League of Legends (2009), Counter-Strike: Global Offensive (2012), Dota 2 (2013), and Overwatch (2016) benefit from skill-based matchmaking. In contrast, Call of Duty: Black Ops II (2012)—a game that primarily focuses on single-player accomplishments—does not benefit from skill-based matchmaking.[8] Treyarch, who developed Call of Duty: Black Ops II, consciously queued players exclusively using ping and latency, in a subversion of industry standards at the time.[9]

Queues

[edit]

In skill-based matchmaking, queue design focuses on how to divide parties into appropriate skill groups. In contrast to StarCraft II (2010), which focuses on player-on-player action, Blizzard Entertainment's Heroes of the Storm (2015) is a team-based game. The Heroes of the Storm matchmaker aims to have players win at least half of the games that they play. The game's matchmaker also aims to pair coordinated teams with other coordinated teams in order to avoid an unfair communication advantage.[10] According to game director Ben Brode, Hearthstone (2014) maintains a separate pool of new players. Players remain in the pool until they win ten games or obtain two legendary minions.

Player rating model

[edit]

The skill rating of a player is their ability to win a match based on aggregate data. Various models have emerged to achieve this. Mark Glickman implemented skill volatility into the Glicko rating system.[11] In 2008, researchers at Microsoft extended TrueSkill for two-player games by describing a number for a player's ability to force draws.[12] Variability in map, character, and server effects have been considered in at least two research papers.[13][14] In 2016, two Cornell University graduates modeled skill rating as a vector of numbers, showing "substantial intransitivity".[15]

Reception

[edit]

Skill-based matchmaking is a controversial practice. In Call of Duty: Warzone (2020), streamers of the game often seek out "bot lobbies"—lobbies with less-skilled players. The Washington Post compared the practice to "LeBron James looking to join pickup games at the local YMCA". Call of Duty: Warzone players who have spoken out against the game's use of skill-based matchmaking include 100 Thieves CEO Nadeshot, former professional Counter-Strike: Global Offensive player Shroud, and 100 Thieves co-owner CouRage. Competitive Call of Duty: Warzone player HusKerrs wrote that high-skilled players must "sweat or try hard" in order to create engaging content.[16] Streamer TimTheTatman refused to stream Call of Duty: Modern Warfare II (2022) upon discovering that it would implement skill-based matchmaking.[17]

In February 2023, Destiny 2 (2017) introduced skill-based matchmaking. Higher-skilled players subsequently discovered a way to enter lobbies with lower-skilled players, resulting in an outcry from the community.[18]

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Skill-based matchmaking

View on Grokipedia
from Grokipedia
Skill-based matchmaking (SBMM) is a computational system employed in online multiplayer video games to pair players against opponents of comparable skill levels, thereby promoting balanced, competitive, and enjoyable gameplay experiences. This approach relies on algorithms that estimate player proficiency through performance metrics such as win rates, kills, and scores, adjusting matches to minimize skill disparities across teams or individuals. The foundations of SBMM trace back to early systems like the Elo rating method, originally developed for chess in the , which was later adapted for video games to handle team-based and multi-player scenarios. advanced this with TrueSkill in 2006, a Bayesian skill rating model implemented in Xbox Live titles such as Halo 3 and Gears of War, enabling rapid skill estimation even for new players by accounting for uncertainties and team dynamics. An improved version, TrueSkill 2, introduced in 2018, incorporates additional factors like player experience, squad composition, and quit behavior to enhance prediction accuracy to 68% for match outcomes, compared to 52% in the original. In practice, SBMM operates by calculating a player's rating post-match based on expected versus actual performance, using metrics like kills per minute (KPM) or score per minute (SPM), and then queuing similar-rated players together while balancing for latency and wait times. For instance, in , is derived from relative performance across the player population, with updates weighted by outcome predictability to reward adaptability and resilience. Advanced implementations, such as those in Ghost Recon Online, extend beyond raw to include behavioral data and preferences for holistic match quality. The primary benefits of SBMM include reduced match blowouts, higher player retention, and decreased quit rates, with data from early implementations showing 80-90% of players achieving better end-of-match placements and extended session times when skill is prioritized. In simulations using Halo beta data, demonstrated faster convergence to stable skill distributions than Elo after just 50 games, fostering competitive incentives and overall engagement. However, tighter skill matching can intensify competition for high-skill players, potentially affecting their enjoyment, though it disproportionately aids novices by preventing discouraging mismatches. By the mid-2020s, SBMM faced substantial criticism in titles like , prompting developers to introduce options for less skill-focused as of 2025.

Overview

Definition

Skill-based matchmaking (SBMM) is a system employed in multiplayer video games to pair players with opponents or teammates of comparable estimated levels, thereby fostering balanced and competitive matches. This approach relies on algorithmic evaluation of player performance to minimize disparities in match outcomes, promoting fairness by reducing the likelihood of one-sided encounters. Unlike random , which assigns players without regard to , SBMM prioritizes equity in competition over expediency or chance. The core components of SBMM include input data derived from player statistics, such as win-loss records, kill-to-death ratios, and overall performance scores accumulated across sessions. These metrics feed into a estimation , typically involving rating systems that quantify a player's proficiency, though the precise methods vary by and are often proprietary. The output consists of assembled lobbies or teams where participants' estimated skills are closely aligned, aiming for an approximate 50% for each side to enhance engagement. SBMM is commonly implemented in genres requiring precise coordination and competition, such as first-person shooters (e.g., ), where balanced groupings help maintain challenge without overwhelming novices or underutilizing experts. Skill rating systems, such as those akin to Elo, provide the foundational estimates but are referenced here only as enablers of the matching process.

Objectives

Skill-based matchmaking (SBMM) aims to promote fair competition by pairing players with opponents of similar levels, thereby creating balanced matches that minimize one-sided outcomes and enhance overall equity. A core objective is to reduce player frustration from mismatched encounters, such as frequent losses against much stronger opponents, which can otherwise lead to disengagement. By fostering environments where players can compete meaningfully, SBMM encourages improvement through challenging yet achievable interactions, allowing individuals to refine abilities without overwhelming discouragement. Ultimately, these goals contribute to maintaining player retention, as balanced experiences keep a broader audience engaged over time. Key principles guiding SBMM include targeting approximate 50% win rates for players to ensure consistent challenge without predictable dominance, achieved through algorithms that balance team compositions based on estimates. This involves adaptive difficulty adjustments, where constraints may loosen if wait times extend, prioritizing over perfect parity to avoid excessive queue delays. Such principles support equitable participation across spectra, with 80-90% of players reporting improved placements and reduced quit rates when is factored in. On a broader scale, SBMM enhances for beginners by shielding them from elite players, while simultaneously providing experts with sufficiently competitive lobbies to sustain interest, thus broadening the game's appeal. This sustained engagement indirectly bolsters strategies, as retained players are more likely to invest in in-game purchases and long-term play. A notable tension in SBMM design lies between upholding competitive integrity—through strict skill alignment—and preserving casual fun, where overly rigid matching can alienate relaxed players seeking low-pressure sessions. Objectives often vary by mode; for instance, ranked modes emphasize precise balancing to support progression and tournaments, whereas unranked modes adopt looser criteria to prioritize quick entry and social enjoyment.

Historical Development

Origins

The origins of skill-based matchmaking can be traced to the chess world, where the challenge of creating balanced tournament pairings necessitated a reliable method for assessing player strengths. In 1960, , a Hungarian-American physicist, chess player, and professor, developed a specifically designed to improve upon the existing Harkness method used by the (USCF). This innovation provided a numerical value representing each player's relative skill as a probabilistic estimate derived from historical game outcomes, enabling organizers to pair competitors with similar ratings for more equitable matches. The system's foundation in statistical modeling allowed it to predict expected results between players, adjusting ratings post-game to reflect actual performance and maintain accuracy over time. The quickly proved its value and was formally adopted by the USCF in , marking the first widespread implementation for organization. By 1970, the International Chess Federation () had endorsed it as the global standard, applying it to international competitions and player classifications. personally oversaw rating calculations for until the mid-1980s, ensuring the system's reliability during its formative years. This adoption underscored the system's role in promoting fair play, as equal-skill pairings minimized lopsided games and maximized competitive integrity. In the 1970s and 1980s, the Elo system extended into early computing applications beyond human tournaments, influencing simulations and evaluations. Computer chess programs, competing in events like the North American Computer Chess Championship starting in 1970, were assigned Elo ratings to gauge their performance against each other and human opponents, facilitating structured pairings in these nascent digital contests. The brought a pivotal shift to fully networked digital platforms, adapting Elo ratings for real-time online matchmaking. The Internet Chess Server (ICS), established in 1992, was among the first to integrate the Elo system for player rankings, automatically pairing users based on their ratings during internet-based games. This development transformed the analog rating concept into a cornerstone of virtual competition, enabling global access to skill-balanced play.

Adoption in Video Games

Skill-based matchmaking in video games began to gain traction in the early 2000s, building on foundational rating systems like Elo, which originated in chess and influenced adaptations for online multiplayer environments. introduced , a Bayesian skill rating system, in 2006 through its research division, deploying it for Xbox Live matchmaking to enhance player pairing beyond the simpler 1-50 ranking used in multiplayer. This marked an early milestone in integrating probabilistic models for more accurate skill assessment in console gaming. In 2008, id Software's highlighted the importance of skill-based matchmaking during development discussions for , emphasizing its role in pairing players of comparable ability to retain newcomers against seasoned competitors. By the mid-2010s, adoption expanded across major titles leveraging online platforms. implemented skill-based queues in upon its 2009 launch, using a hidden matchmaking rating (MMR) derived from Elo principles to form balanced teams in normal and ranked modes. integrated SBMM into in 2013 via its Steamworks platform, introducing ranked matchmaking that required about 150 games to unlock and relied on MMR for equitable opponent selection in modes like All Pick and Captain's Mode. followed suit with in 2016, launching Competitive Play in Season 2 that incorporated skill tiers and MMR-based matchmaking to group players by relative proficiency. Key events underscored evolving priorities in implementation. opted against full SBMM in (2010), focusing instead on connection quality to minimize queue times and prioritize accessible multiplayer sessions over strict skill balancing. This approach shifted in Call of Duty: Black Ops II (2012), where and revamped matchmaking to emphasize ping and latency exclusively, moving away from locks to ensure low-latency games while deprioritizing as the primary factor. Recent developments through 2025 reflect ongoing experimentation and refinements. In Pokémon UNITE's Season 23 (late 2024), The Pokémon Company announced a shift away from traditional MMR-based , transitioning to a system reliant solely on Master rank ratings starting in Season 24 to streamline high-level play and reduce wait times. revamped Deadlock's MMR system in December 2024, unifying matchmaking pools across normal and ranked modes while incorporating hero-specific strengths and weaknesses for more nuanced pairings. In contrast, introduced OG's Expert Duos mode in 2024, deliberately excluding SBMM and bots to recreate the unstructured, high-stakes lobbies of early seasons.

Technical Mechanisms

Skill Rating Systems

Skill rating systems form the foundational component of skill-based matchmaking by assigning numerical values to players' abilities, enabling fair opponent pairing. These systems update ratings based on game outcomes, incorporating factors such as win/loss results, opponent strength, and uncertainty to reflect skill more accurately over time. Primary models like Elo and Glicko provide straightforward mechanisms for individual player assessment, while advanced variants such as and the Cornell model extend capabilities to handle team dynamics, draws, and contextual intransitivities. The , developed by in the for chess, represents one of the earliest and most influential approaches. It operates on a logistic model where a player's rating adjusts after each game based on the actual outcome compared to the expected outcome against the opponent. The core update formula is: Rnew=Rold+K×(S−E)R_{\text{new}} = R_{\text{old}} + K \times (S - E) Here, RnewR_{\text{new}} and RoldR_{\text{old}} are the updated and prior ratings, KK is a constant factor determining adjustment sensitivity (typically 32 for beginners and lower for experts), SS is the actual score (1 for win, 0.5 for draw, 0 for loss), and EE is the expected score calculated as: E=11+10(Rb−Ra)/400E = \frac{1}{1 + 10^{(R_b - R_a)/400}} where RaR_a and RbR_b are the ratings of the player and opponent, respectively. This system assumes binary or ternary outcomes and promotes convergence toward stable ratings through iterative updates, though it does not explicitly model uncertainty in new or infrequent players. Building on Elo's limitations, the Glicko system, introduced by Mark Glickman in 1995, incorporates rating deviation (RD) to quantify uncertainty and volatility in skill estimates. RD starts high for new players (e.g., 350 points) and decreases with consistent performance, allowing conservative adjustments for those with limited data. The update process uses a Bayesian framework to revise both the rating and RD based on game results, opponents' ratings, and their deviations, making it more robust for sparse match histories. Glicko-2, an adaptation, introduces a per-player volatility parameter to model potential skill changes over time and extends handling of non-binary outcomes, such as multi-player scores. TrueSkill, developed by in 2006, advances rating through a fully Bayesian probabilistic model suitable for team-based games and draws. Each player is represented by a distribution with μ\mu (average ) and standard deviation σ\sigma (uncertainty), initialized at μ=25\mu = 25 and σ=25/3\sigma = 25/3 for conservatism. After a match, posterior distributions are computed using approximate in a , accounting for team performance and partial outcomes without requiring exact differences. This enables handling of multi-player scenarios and draws by modeling win probabilities via a Gaussian on differences. TrueSkill's design ensures scalability for online platforms like Xbox Live, emphasizing uncertainty reduction over time. The Cornell model, proposed by researchers at Cornell University in 2016, addresses intransitivity in matchups by representing skill as a vector rather than a scalar, capturing context-specific strengths such as performance on particular maps or against certain playstyles. Using a Bradley-Terry-like framework extended to vectors, it learns pairwise preference relations from matchup data, allowing predictions of non-transitive outcomes (e.g., rock-paper-scissors dynamics in games). This vector approach models skill as a low-dimensional embedding, enabling more nuanced ratings that adapt to environmental factors without assuming total orderings.

Matching Algorithms

Skill-based matchmaking algorithms utilize player skill ratings, typically derived from systems like Elo or , to pair individuals or teams in online s. The core process begins with queue formation, where players entering a matchmaking queue are categorized by mode, region, and availability. Algorithms then apply skill bucketing to group players within narrow rating ranges, such as ±50 points, to ensure competitive balance while minimizing wait times. For team-based s, balancing occurs by sorting queued players by skill and assigning them alternately to opposing teams, often using a greedy approach that adds each subsequent player to the team with the current lowest aggregate skill sum to prevent imbalances. Beyond primary skill considerations, algorithms incorporate secondary factors to enhance match quality and player retention. Latency (ping) is frequently prioritized to avoid network disadvantages, with thresholds like under 100 ms enforced for pairings. Other elements, such as playstyle preferences or hardware capabilities, may influence selections in specialized systems, though skill remains dominant. Trade-offs between match fairness and wait times are managed dynamically; for instance, broader skill buckets expand during low population periods to reduce queues from minutes to seconds, as modeled by formulas estimating wait time based on online player count and bucket granularity. Common algorithms include greedy matching, which iteratively pairs the highest-rated available player with a suitable opponent within a predefined gap, enabling rapid queue resolution suitable for high-volume games. For more optimized outcomes, techniques formulate matchmaking as a minimum weight problem on a graph of player nodes, where edges represent predicted match quality (e.g., or engagement risk), solved in polynomial time to maximize overall balance across multiple pairs. A simple example for Elo-based greedy pairing in a 1v1 queue might proceed as follows:

function greedyPairing(queue): sort queue by Elo descending matches = [] i = 0 while i < queue.length - 1: player1 = queue[i] for j = i+1 to queue.length: player2 = queue[j] if abs(player1.Elo - player2.Elo) <= threshold: matches.add((player1, player2)) remove player1 and player2 from queue break i += 1 return matches

function greedyPairing(queue): sort queue by Elo descending matches = [] i = 0 while i < queue.length - 1: player1 = queue[i] for j = i+1 to queue.length: player2 = queue[j] if abs(player1.Elo - player2.Elo) <= threshold: matches.add((player1, player2)) remove player1 and player2 from queue break i += 1 return matches

This approach ensures pairs stay within skill thresholds but may leave outliers unpaired if queues are sparse. To handle performance variability, algorithms apply volatility adjustments, such as higher K-factors in Elo systems for new players (e.g., K=32 for beginners versus K=16 for veterans), accelerating convergence toward true without overreacting to noise. New player protections mitigate initial rating uncertainty by confining matches to provisional pools or inflating buffers, allowing novices several games (e.g., 5-10) before full integration, as uncertainty in skill estimates diminishes rapidly with experience. These mechanisms, implemented in platforms like Xbox Live, promote equitable progression while sustaining engagement.

Implementations

In Esports Titles

Skill-based matchmaking (SBMM) plays a pivotal in titles, where competitive integrity is paramount for fair ladder progression, tournament qualification, and professional play. In these environments, SBMM systems pair players based on metrics to ensure balanced that reflect true ability rather than luck or mismatched opponents, often integrating with broader features like leaderboards and anti-cheat mechanisms. League of Legends, since the introduction of its ranked queues in late 2009 shortly after the game's launch, has employed SBMM through a combination of visible League Points (LP) and a hidden Matchmaking Rating (MMR). Players earn or lose LP based on match outcomes, with MMR adjusting dynamically to form teams of comparable skill within tiered divisions, from Iron to Challenger, facilitating precise for millions of competitive users. This system underpins the game's scene, including the League of Legends Championship Series (LCS), by providing reliable skill assessments for draft picks and seeding. Similarly, introduced its MMR system in December 2013 to enhance ranked matchmaking, assigning numerical ratings that increase with wins and decrease with losses to create equitable games. In esports contexts, such as The International tournaments, MMR serves as a key metric for player seeding and team composition, ensuring high-stakes matches feature appropriately skilled competitors and reducing variance in outcomes. The system uses Glicko-2 methodology for variance estimation, which helps in calibrating new players accurately for tournament eligibility. Overwatch's Competitive mode, launched on June 28, 2016, utilizes Skill Rating (SR) tiers ranging from to Grandmaster, with SBMM grouping players by SR to promote skill-congruent teams in 6v6 matches. This setup supports the by enabling precise role-based and progression tracking, where SR adjustments reflect performance modifiers beyond mere wins. In , released in June 2015, SBMM adaptations emphasize rapid queue times while targeting balanced engagements, resulting in approximate 50% win rates as players converge to their true skill level through iterative MMR adjustments. Blizzard's design encourages this equilibrium without explicit forcing, prioritizing fair compositions in hero-based battles for competitive ladders. Esports SBMM implementations have faced exploits, as seen in Destiny 2's PvP modes around 2022, where players manipulated connection drops to force rematches with lower-skill lobbies, leading to patches that tightened SBMM parameters and introduced penalties for intentional disconnects to preserve match integrity. Unique to esports, SBMM integrates with spectator tools by generating predictably competitive matches that heighten viewer engagement, as balanced games reduce blowouts and highlight strategic depth in broadcasts. Anti-smurfing measures, such as mandatory account verification and behavioral analysis in titles like League of Legends and Dota 2, detect and penalize alternate accounts to maintain ladder purity. Regional server balancing further refines global tournaments by routing players to low-latency data centers based on skill pools, minimizing ping disparities during events like Worlds or majors. A notable recent development occurred in Pokémon's competitive ecosystem in December 2024, when updates to Pokémon TCG Live's Seasonal shifted higher tiers to an Elo-based ranking system for the Arceus League, aiming to create fairer and more accurate stratification for qualifiers and regional championships.

In Casual Games

In casual games, -based (SBMM) is implemented to foster engaging experiences for broad audiences by pairing players of comparable abilities in non-competitive modes, thereby supporting retention without the intensity of ranked play. For instance, in : Warzone's public matches launched in 2020, SBMM evaluates player performance metrics such as kills, deaths, and win rates to form balanced lobbies, prioritizing fair competition alongside low latency connections. Similarly, Hearthstone's casual queues, introduced with the game's 2014 launch, utilize a hidden matchmaking rating (MMR) system to match opponents of similar levels, incorporating factors like win streaks to adjust pairings and prevent prolonged dominance. Fortnite's core battle royale modes, such as solos and duos, have employed SBMM since a 2019 update to create equitable matches based on eliminations, survival times, and overall performance, with the system dynamically incorporating bots for newer players to ease entry. These implementations often feature lighter variations to emphasize fun and social interaction; for example, Roblox's matchmaking framework, enhanced in 2023, allows developers to weight social connections—such as friends or preferred groups—over strict skill metrics in user-generated experiences, promoting collaborative play in casual environments. Some titles provide opt-out mechanisms or relaxed SBMM in casual playlists, as seen in XDefiant's unranked modes, where developers explicitly omitted skill considerations to prioritize quick, varied matches. To balance accessibility, SBMM in party modes typically averages the group's skill levels rather than enforcing individual precision, enabling mixed-ability friends to enjoy casual sessions without severe imbalances. In , the October 2025 update introduced split matchmaking for multiplayer battles, with casual Regular Battles focused on loot collection and no trophy loss, alongside a separate Ranked Battles option (from 7) that uses levels for skill-based matching in competitive play. This approach contrasts with modes designed for variety, such as Fortnite's OG Expert Duos introduced in July 2025, which eliminates SBMM and bots to deliver unpredictable, old-school matchmaking for players seeking diversity beyond balanced queues.

Advantages and Challenges

Benefits

Skill-based matchmaking (SBMM) enhances match quality by pairing players of comparable skill levels, resulting in more balanced games and approximately 50% win rates for most participants. This balance minimizes one-sided matches, or "blowouts," where score differentials exceed 30 points, fostering a sense of fairness that encourages continued play. In games like , tightening skill constraints in matchmaking leads to 80-90% of players achieving better end-of-match placements and lower quit rates, directly boosting retention across skill distributions. SBMM also provides clear visibility into skill progression through accurate rating systems, allowing players to track improvements and set achievable goals. Microsoft's algorithm, deployed in Halo titles, achieves 68% accuracy in predicting match outcomes, enabling precise skill assessments that highlight personal growth and motivate ongoing participation. By creating equitable lobbies, SBMM contributes to lower churn rates when opponent skill variance is minimized, as balanced matches reduce player dropout compared to highly mismatched games. For newcomers, this setup accelerates learning curves, as players face appropriate challenges that build competence without discouragement, leading to faster skill acquisition and higher engagement. These dynamics sustain by extending play sessions; balanced matches encourage longer , with studies showing players stick with titles longer when skill is prioritized, supporting the long-term health of online communities.

Criticisms

One prominent criticism of skill-based matchmaking (SBMM) is that it creates overly competitive "sweaty" environments for skilled players, denying them occasional easy wins and turning casual sessions into high-stakes battles. In games like : III, the system's focus on "perfect matches" results in lobbies with minimal skill variance, often leading to narrow score margins and increased stress without the relaxation of mismatched opponents. Similarly, top-tier players report constant intensity, as the algorithm prioritizes even team compositions over diverse experiences. SBMM also incentivizes smurfing, where experienced players create alternate accounts to bypass the system and access easier lobbies. This behavior arises because high-skill individuals face relentless challenges in standard queues, prompting them to seek casual play through new profiles, which undermines fair for beginners. Prior to 2025, the absence of non-SBMM casual modes in many titles exacerbated this, as players resorted to smurfing for variety rather than enduring perpetual try-hard encounters. Longer queue times, particularly in low-population regions or for elite players, represent another key drawback. High-skill users often wait extended periods due to the scarcity of suitable opponents, with former Halo multiplayer lead Max Hoberman describing this segregation as "a form of " that isolates top performers from the broader player base. In titles like , prioritizing skill over availability can inflate waits, leading players to abandon queues and fragment the further. Controversies have highlighted these issues, notably streamer backlash in Call of Duty: Warzone during its 2020 launch. Influencer publicly demanded "bot lobbies" and a ranked mode, arguing that SBMM's strict enforcement made streaming untenable by forcing constant high-level competition without casual alternatives. His complaints amplified community frustration, dividing players over whether SBMM stifles content creation and enjoyment. In , perceived manipulation via SBMM exploits emerged around 2022-2023, allowing skilled fireteams to infiltrate low-skill PvP lobbies through matchmaking flaws introduced in Season 18, resulting in one-sided stomps that frustrated newcomers. Technical flaws include inaccurate skill ratings for new players, as algorithms rely on limited performance data, potentially misplacing them in mismatched games until sufficient matches accumulate. Matching algorithms can also force trade-offs between skill balance and connection quality, prioritizing low ping but occasionally pairing players with distant servers, leading to laggy matches when ideal skill pools are unavailable. Privacy concerns arise from the extensive tracking of player stats required for SBMM, raising fears of misuse. Activision's 2021 shutdown of the SBMM Warzone stat-tracking site cited violations of laws in the and , emphasizing risks from unauthorized access to detailed performance metrics. Demands for SBMM toggles have grown, as seen in 2024 OG discussions, where players criticized bot-adjusted lobbies for eroding nostalgic play and called for options to customize matchmaking rigor. In response to ongoing criticisms, as of November 2025, 7 has implemented changes to SBMM, making it no longer the default. The game features open playlists for casual multiplayer without skill-based considerations, alongside standard modes that retain SBMM, aiming to provide variety, reduce queue times for high-skill players, and mitigate sweaty lobbies while preserving competitive balance where desired.

Community Response

The community response to skill-based matchmaking (SBMM) has been notably polarized, with casual players often praising it for fostering balanced and engaging matches that reduce from mismatched opponents, while experienced or players frequently criticize it for creating overly competitive "sweaty" lobbies that diminish the fun of casual play. This divide became particularly evident in the late and early , as SBMM's implementation in major titles amplified debates over player retention versus enjoyment. A prominent example of frustration among veterans occurred in 2020 with Call of Duty titles, where players launched petitions demanding the removal of SBMM, arguing it forced reliance on meta strategies and hindered social play with friends of differing skill levels. Social media trends on platforms like and further highlighted this discontent, with hashtags and threads decrying SBMM as overly punitive, leading developers to publicly address the feedback by explaining its role in combining skill with factors like latency. In contrast, the esports community around has shown stronger support for SBMM, particularly in ranked queues, where it is viewed as essential for fair competition and skill progression, with ' matchmaking system routinely praised in forums for maintaining balanced 50% win rates. Data from Activision's experiments underscores the approval among casuals, revealing that deprioritizing SBMM led to an 80% increase in quit rates across 80% of players, with reduced retention for the bottom 90% of skilled players (primarily lower-skilled ones), indicating broad retention benefits in non-esports contexts. Streamers have amplified these criticisms, influencing public discourse and contributing to viral trends that pressure developers, though quantitative polls remain limited. Cultural differences also shape responses, with Asian gaming communities—particularly in titles like —embracing SBMM's ranked focus due to a stronger emphasis on competitive progression and higher overall player skill levels compared to Western audiences, where casual relaxation often takes precedence. This evolution reflects a shift from relative acceptance in the , when SBMM was seen as a novel fairness tool in early implementations like : Advanced Warfare, to intensified 2020s debates fueled by larger player bases, influencer voices, and data-driven defenses from publishers.

Emerging Developments

In 2024, significantly overhauled the and MMR system in its MOBA game Deadlock to address imbalances and improve fairness, introducing AI-enhanced predictions that account for individual player strengths and weaknesses across different heroes. This update unified matchmaking pools into a single mode, eliminating separate normal and ranked queues, and incorporated more nuanced skill assessments to create balanced teams without time restrictions on queuing. Hybrid systems blending strict SBMM with random elements have gained traction as developers seek to balance competitive integrity with gameplay variety. For instance, Call of Duty: Black Ops 7, released on November 14, 2025, launched with "open " as the default in most playlists, minimizing skill-based considerations to allow for more randomized lobbies while offering persistent lobbies and optional ranked modes (with standard SBMM) for those preferring structured matches. This approach represents a shift toward hybrid models that reduce the rigidity of traditional SBMM, responding to player feedback on overly predictable experiences. Emerging trends include greater player control through opt-in or features for SBMM intensity, as seen in Black Ops 7's default open system that effectively opts players out of heavy skill weighting unless they choose ranked play. Cross-platform unification efforts continue to evolve, with games like maintaining SBMM across console and PC pools to ensure consistent experiences despite hardware differences, though challenges in unifying skill metrics persist. Additionally, Pokémon Unite's Season 23 update in December 2024 shifted from pure MMR-based SBMM to a rank-only system using Master rank ratings, aiming to simplify and reduce over-reliance on hidden skill calculations for more transparent competition. Looking ahead, future developments may see a decline in strict SBMM to prioritize variety, exemplified by Fortnite's 2024 OG mode update, which reintroduced bots into lobbies for lower-skilled players alongside SBMM to foster more diverse and less punishing matches. Ethical AI integration for bias reduction is also on the horizon, with 2025 research highlighting the need for fair algorithms in SBMM to mitigate discriminatory outcomes in player pairing, particularly in AI-driven systems used in fantasy sports and multiplayer games. Projections for 2025 suggest continued experimentation, such as potential behavioral metric expansions beyond traditional stats—like engagement patterns and decision-making styles—to refine matchmaking in titles like .

References

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  32. https://www.redbull.com/us-en/the-league-ranked-system-explained
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