Betting Trends Analysis

Betting Trends in 2 Minutes

Betting trends are patterns in historical results that bettors use to inform their decisions. They include against-the-spread records, over/under tendencies, home and away splits, public betting percentages, and situational angles like rest advantages or divisional rivalry performance. When used correctly, trends help identify situations where the odds may not fully reflect the likely outcome.

This guide covers the major types of betting trends, how to read and interpret them, and how to apply them without falling into common traps. You will learn which trends are worth paying attention to, which ones mislead more than they help, and how to build a research process that combines trend data with game-level context.

Key concepts covered:

• Against the spread (ATS) and totals trends
• Situational angles like rest, travel, and schedule spots
• Public betting percentages and reverse line movement
• Sample size requirements and statistical significance
• How to combine trends with expected value analysis

Trends are a tool, not a crystal ball. They reveal tendencies over large sample sizes, but no trend guarantees a specific outcome. The goal is to use trend data alongside other research to make more informed bets. Treat all sports betting as entertainment first, and never wager more than you can afford to lose.

What Are Betting Trends?

A betting trend is any measurable pattern in past results that bettors use to evaluate future matchups. At its simplest, a trend is a statement like "Team X is 8-2 against the spread in their last 10 home games" or "the over has hit in 7 of the last 9 meetings between these two teams."

Trends come from compiling game results, point spreads, totals, and betting market data over time. They cover a wide range of angles:

• Team performance trends - How a team has performed against the spread, on totals, or in specific game situations over a defined period
• League-wide trends - Patterns that hold across an entire sport, such as home underdogs covering at a historically higher rate
• Situational trends - Results filtered by context like rest days, travel distance, time of season, or opponent type
• Betting market trends - Where the public is putting money and how lines move in response

The data behind betting trends comes from publicly available results, sportsbook archives, and third-party databases. Most major sports data providers track ATS records, totals results, and situational splits going back years or decades.

Where to find trend data:

• Sportsbook statistics pages and pre-match data sections
• Third-party databases and analytics sites
• League and team statistical resources
• OddsIndex odds comparison tools for tracking line movement

Understanding that trends are descriptive rather than predictive is critical. A trend describes what happened in the past. It does not cause future results. The value of a trend depends entirely on whether the underlying conditions that created it still exist.

Types of Betting Trends

Against the Spread Trends

ATS trends track how teams perform relative to the point spread rather than just wins and losses. A team with a 10-4 record might only be 6-8 ATS if they frequently win by less than the spread suggests.

ATS trends are useful because they focus on betting outcomes rather than game outcomes. A team that wins often but fails to cover is a poor betting target for spread bettors.

Common ATS trend filters include:

• Home vs away splits
• As a favorite vs as an underdog
• Conference or divisional games
• After a win vs after a loss
• In the first or second half of the season

Example: An NFL team is 12-4 ATS as a home underdog over the last three seasons. This suggests the market consistently undervalues them at home, which could indicate value the next time they are a home dog.

ATS trends can also reveal profitability that straight win-loss records hide. The 2018 Buffalo Bills finished 6-10 on the season, but their ATS record made them one of the most profitable teams to bet on that year. The market consistently set expectations too low for Buffalo, and bettors who tracked that ATS trend profited despite the team's losing record. This illustrates why ATS records matter more than wins and losses for trend analysis.

Over/Under Trends

Totals trends track whether games involving certain teams, matchups, or situations tend to go over or under the posted total. Scoring tendencies, pace of play, defensive efficiency, and weather all influence totals outcomes.

Useful totals trend angles include:

• Team-specific over/under records
• Indoor vs outdoor game scoring patterns
• Division rival matchups (often lower scoring due to familiarity)
• Back-to-back or short rest situations
• Early season vs late season scoring rates

Totals trends can reveal systematic mispricing. If a team consistently plays in games that go under because their defense is better than the market prices, the under may offer recurring value.

Home and Away Splits

Home field advantage varies by sport and has changed over time. Tracking home and away performance helps calibrate how much weight to give the venue.

In the NFL, home field advantage has declined in recent years, but certain teams still perform significantly better at home due to altitude, weather, noise, or travel demands on opponents. In college sports, home field advantage remains substantial.

Away team fatigue, time zone changes, and hostile environments all factor into home/away splits. Cross-country road trips affect performance more than short regional travel.

Situational Trends

Situational trends filter results by specific circumstances surrounding a game. These are among the most actionable trends because they isolate factors the market may underweight.

Rest advantages - Teams with extra rest days (coming off a bye week in the NFL, for example) have historically performed better ATS than teams on standard rest. The advantage is even larger when one team has rest and the opponent does not.

Scheduling spots - A team playing a perceived weaker opponent between two high-profile matchups (a "trap game") sometimes underperforms. Teams looking ahead to a rival or playoff opponent may not play with full intensity.

Revenge games - Teams returning to face a former coach or a team that eliminated them from the playoffs sometimes show elevated motivation. The data on revenge games is mixed, but specific contexts can be meaningful.

Travel and time zones - West coast teams traveling east for early kickoffs have historically underperformed, particularly in the NFL. Jet lag and disrupted routines affect performance.

Public Betting Trends

Public betting percentages show what percentage of bets or money is on each side of a market. This data comes from sportsbooks and aggregation sites that track handle (money wagered) and ticket count (number of bets placed).

Public bettors tend to favor favorites, overs, and popular teams. When public money is heavily one-sided, the line may adjust to balance the book's exposure rather than reflecting the true probability. This creates potential value on the less popular side.

Ticket percentage vs money percentage:

Public betting data typically shows two distinct metrics. Ticket percentage reflects the number of individual bets placed on each side, while money (handle) percentage shows the total dollars wagered on each side. When these two numbers diverge, it reveals important information. For example, if 80 percent of tickets are on the favorite but only 55 percent of the money is on that side, the remaining 45 percent of money came from fewer but larger wagers, likely from sharps. This ticket-money split is one of the most reliable signals for identifying which side professional bettors favor.

Fading the public (betting against the majority) has been a profitable long-term strategy in certain contexts, particularly when combined with other indicators like line movement. For a deeper look at how sharp and public bettors differ, see our sharp vs square betting guide.

Injury and Lineup Trends

Player availability affects game outcomes and betting lines, but the market does not always adjust lines enough when key players are absent. Tracking how teams perform without their star players reveals whether the market over- or under-adjusts.

For instance, an NBA team without their starting point guard might be 7-3 ATS because the market overreacts to the absence and moves the line too far. Alternatively, an NFL team without their starting quarterback might be 2-8 ATS because the drop-off is genuinely severe.

Understanding how injuries shift lines and outcomes is a valuable research angle. For a deeper look at this topic, see our injury impact on betting guide.

How to Read and Interpret Betting Trends

Sample Size Matters

The most important concept in trend analysis is sample size. A trend based on 5 games is nearly meaningless. A trend based on 500 games carries real statistical weight.

General sample size guidelines:

• Fewer than 20 games - Too small to draw conclusions. Random variance dominates.
• 20-50 games - Suggestive but not conclusive. Worth noting as supporting evidence.
• 50-100 games - Reasonably meaningful, especially for broad situational trends.
• 100+ games - Strong enough to identify genuine tendencies.

Be skeptical of any trend presented with a small sample. "Team X is 4-0 ATS in Thursday night road games" sounds impressive but is statistically meaningless.

Correlation vs Causation

A trend existing does not mean the factors you associate with it caused the outcome. A team might be 9-1 ATS in games where the temperature is below 40 degrees, but the cold weather might not be the reason. Perhaps those games happened to be against weak opponents, or the team had favorable rest in each case.

Always ask: is there a logical reason this trend should continue? If you cannot identify a mechanism connecting the trend to outcomes, the pattern may be coincidence.

Recency Bias vs Long-Term Patterns

Recent results feel more relevant than older results, and sometimes they are. Roster changes, coaching changes, and scheme adjustments make last season's data more relevant than data from three seasons ago.

However, recency bias also leads bettors to overweight small recent samples. A team that covered 5 straight games is not necessarily on a hot streak with predictive power. Five games is noise, not signal.

The best approach balances recent data with longer-term patterns. Weight recent results more heavily, but do not ignore multi-season trends that have a logical basis.

Context Behind the Numbers

Raw ATS records without context can mislead. A team with a 10-2 ATS record might have faced an easy schedule, caught favorable line movements, or benefited from opponent injuries. Strip away the headline number and examine the games individually.

Ask these questions when evaluating any trend:

• What was the quality of competition?
• Were there unusual circumstances (weather, injuries, neutral sites)?
• Has the roster or coaching staff changed since these results?
• Has the market adjusted to this trend already?

Using Betting Trends for Expected Value

Trends become valuable when they help you identify situations where the odds do not accurately reflect the true probability of an outcome. This connects directly to expected value (EV) betting. For a comprehensive look at EV concepts, see our expected value betting guide.

How trends reveal mispriced lines:

If a well-supported trend shows that a particular situational angle produces results that differ from the line's implied probability, there may be value. For example, if NFL home underdogs of 3 to 7 points cover 55 percent of the time over a 10-year sample, but the implied probability from the odds is only 48 percent, that gap suggests positive expected value.

Combining trends with current game context:

No trend operates in isolation. The best approach uses trends as one input alongside injury reports, matchup analysis, weather conditions, and line movement. A favorable trend that aligns with your independent analysis of the game strengthens the case. A trend that contradicts everything else you see is less actionable.

Building a trend-based research process:

1. Start with the matchup and form an initial assessment based on team quality and situation
2. Check relevant situational trends (rest, home/away, ATS in similar spots)
3. Look at public betting percentages and line movement
4. Determine whether trends support, contradict, or are neutral to your initial read
5. Size your bet (or pass) based on the overall strength of the case

When trends support the line:

Sometimes trend analysis confirms that the line is accurate. There is no value in betting a side that trends and current analysis both suggest is fairly priced. Passing on a game is a valid outcome of your research.

Historical Betting Trends That Matter

Some league-wide trends have persisted over long time periods and have logical explanations for why they continue. For a deeper exploration of long-term patterns, see our historical betting trends guide.

Home underdogs:

Across major sports, home underdogs have historically covered at a rate slightly above 50 percent. The theory is that the market undervalues home field advantage for weaker teams. The public prefers to bet favorites, which can push lines slightly past fair value. In soccer, this trend has been particularly trackable. During the 2020-21 English Premier League season, home teams were underdogs in 131 of 380 matches. Those home underdogs won or drew in 80 of those games at average odds around 1.83, producing roughly 15 units of profit over the full season for bettors who backed them consistently.

Unders in low-total games:

Games with low posted totals (NFL unders of 40 or less, for instance) have historically leaned toward the under. The logic is that sportsbooks set low totals for defensively strong matchups, and those defenses tend to perform as expected.

Rest advantages:

Teams with rest advantages, whether from bye weeks, extra days off, or scheduling quirks, have a measurable ATS edge. Rest allows for better preparation, healthier rosters, and more focused game plans.

Divisional familiarity:

Games between division rivals tend to be closer than the line suggests, particularly when one team is a large favorite. Familiarity breeds competitiveness, as teams in the same division play each other multiple times and adjust their strategies accordingly.

Late-season motivation:

Teams fighting for playoff positioning in the final weeks of the season outperform eliminated teams, which is intuitive but still underpriced by the market in some situations.

Sharp vs Public Betting Trends

Understanding who is on each side of a bet provides important context. Sharp bettors (professionals) and public bettors (recreational) behave differently, and their actions leave traces in line movement and betting percentages.

Public tendencies:

• Bet favorites, especially popular teams
• Bet overs (scoring is exciting)
• Bet based on recent results and name recognition
• Place most bets close to game time

Sharp tendencies:

• Bet underdogs and unders more frequently than the public
• Move lines with large wagers early in the week
• Focus on closing line value rather than results
• Bet based on models, data, and long-term edge

Reverse line movement:

When the line moves in the opposite direction of where the majority of bets are placed, this signals sharp money on the less popular side. For example, if 75 percent of bets are on the favorite but the line moves from -7 to -6.5, sharps likely bet the underdog heavily enough to move the number. Understanding line movement is a key part of trend analysis. See our line movement in betting guide for more on this topic.

When to fade the public:

Fading the public works best in high-profile games where casual bettors are most active. Primetime NFL games, major college football matchups, and playoff games attract the most public action. Look for situations where one-sided public betting pushes the line past fair value.

Fading the public does not work in every situation. Low-profile games attract less public money, making the contrarian angle weaker. Always combine public betting data with other research rather than blindly betting against the majority.

Common Mistakes When Using Betting Trends

Over-relying on Small Sample Sizes

The most frequent mistake is treating a trend based on a handful of games as meaningful. "5-0 ATS in their last 5 games" tells you almost nothing. Always check how many games support a trend before acting on it.

Ignoring Context Behind the Numbers

A headline ATS record does not tell you why a team covered or failed to cover. Maybe a team went 8-2 ATS, but 6 of those 8 covers came against teams missing key players. Without context, the trend is misleading.

Cherry-Picking Trends

With enough data filters, you can find a trend to support any bet. "Team X is 7-1 ATS in road games on artificial turf when coming off a loss in the month of October" is a meaningless construction. Genuine trends are simple, have logical backing, and survive without excessive filtering.

Treating Trends as Guarantees

Past results do not determine future outcomes. Even a strong, well-supported trend covering 55 percent of the time means you will lose 45 percent of those bets. Trends shift your probability assessment, they do not provide certainty.

Ignoring Market Adjustment

If a trend becomes widely known, the market adjusts. A trend that was profitable five years ago may already be priced into the line today. The betting market is efficient, and edges erode as information spreads.

Not Tracking Your Results

If you use trends to inform your bets, track the results. Over time, you will learn which trend types produce value for you and which ones do not. Without tracking, you are guessing about what works.

Frequently Asked Questions

What are betting trends?

Betting trends are patterns in historical results that bettors use to evaluate upcoming games. They include against-the-spread records, over/under tendencies, home/away splits, public betting percentages, and situational angles like rest advantages. Trends describe what happened in past games under similar conditions, which can help inform future betting decisions.

How do I find reliable betting trend data?

Reliable trend data comes from reputable sports analytics sites, sportsbook statistics pages, and established databases that track results over multiple seasons. Look for sources that show sample sizes alongside their trends and present raw data rather than cherry-picked narratives. Avoid trend claims that do not specify how many games the data covers.

How many games do I need for a trend to be meaningful?

As a general guideline, trends based on fewer than 20 games are unreliable due to random variance. Trends with 50 to 100 games are reasonably meaningful, and those with 100 or more games carry significant statistical weight. The more games supporting a trend, the more confidence you can place in it reflecting a genuine pattern rather than luck.

Can betting trends predict outcomes?

Trends cannot predict specific game outcomes. They identify historical tendencies that may give one side a slight edge over many games. A trend showing 56 percent ATS coverage still means the other side wins 44 percent of the time. Use trends to inform probability assessments, not to guarantee results.

Should I always fade the public?

No. Fading the public (betting against the majority) is most effective in high-profile games where casual bettors inflate one side. In lower-profile games, public action has less impact on the line. Blindly fading the public without additional analysis is not a profitable long-term strategy. Combine public betting data with line movement, injury reports, and your own research.

What is the most important betting trend to follow?

No single trend is universally most important. The value of a trend depends on your sport, bet type, and the specific situation. That said, rest advantages, home underdog performance, and reverse line movement are among the most consistently cited trends by experienced bettors because they have logical explanations and long-term data support.

How do I know if a trend is already priced into the line?

If a trend is widely known and discussed, sportsbooks and sharp bettors have likely already incorporated it into the line. Check whether the trend still produces results above the implied probability of the current odds. If the line already reflects the trend, there is no remaining value to exploit.

Do betting trends actually work?

Betting trends work as probability adjusters, not game-by-game predictors. A well-supported trend backed by hundreds of games and a logical explanation can give you a small edge over the market when combined with other research. However, no trend works in isolation, and even strong trends lose roughly 40-45 percent of the time. Trends are most effective when used as one input in a broader analysis process rather than as a standalone betting system.

Do betting trends work the same across all sports?

Trends operate differently across sports because scoring systems, sample sizes, and variance levels differ. NFL trends involve smaller sample sizes (17-game season) than NBA or MLB trends (82 and 162 games). Lower-scoring sports like hockey and soccer have higher variance per game than basketball. Always evaluate trends within the context of the specific sport you are betting.