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How to predict basketball totals


Here's formula for better success on NBA totals - VSiN Exclusive News - News

Sports bettors trying to beat Over/Unders for the first time often make the same type of mistake. Whatever the sport, they’re prone to look at team scoring averages without context. 

Here’s a quick recent example from the NBA. Both the Sacramento Kings and Atlanta Hawks have seen an average of around 229 points scored in their games this season (meaning the scoring sums in their games total that amount…maybe a win of 115-114, a loss of 120-109, etc..). If those teams are playing each other, the novice gambler will make a knee-jerk assumption that they should total around 229 against each other. 

“How easy is THIS?! Sacramento’s games average 229, Atlanta’s games average 229…they’re obviously going to play to 229 against each other!”

Then the market brings an Over/Under of 236.  

“Ha! Those so-called sharps are missing an easy winner. The Under is a gift!”

The Kings and Hawks played back on Jan. 30 against a market total of 236, flying Over in a 135-113 Sacramento rout. 

You can’t average “extreme” teams together like that. The NBA league average this season is around 221. A team that plays high scoring games “on average” is likely to really blow up when playing another high-scoring team. 

Visualize it this way. Let’s separate the league into teams that play “high scoring” games, teams that play “average scoring” games, and teams that play “low scoring” games. Sacramento qualifies as high scoring this season in the high 220’s. The Kings will get dragged down below that vs. teams that play “low-scoring” games…will settle right around their normal 229 vs. “average scoring” teams…but will blow up much higher vs. other “high-scoring” teams.

The market guessed around 236 for the blow up vs. Atlanta, and undershot the mark. 

The same general principle is true in college basketball, college football, pro football, and hockey (baseball’s trickier because of varying pitching depth and ballpark effects). The extremes drive each other higher at the top of the spectrum, and lower at the bottom of the spectrum. That’s why you see such a volatile range of Over/Unders in those sports that skew much wider than basic averages would suggest.

Some junior high algebra can help you make rough estimates. The formula is simply Team A’s average game scoring total…times Team B’s average game scoring total…divided by the league average. If you play with the numbers, you’ll see that the market is already taking this phenomenon into account.  

For our Atlanta/Sacramento example, 229 times 229 divided by 221 is 237.

That’s very close to what the market posted. If a pair of “low scoring” 215 teams played each other, that formula would spit out a low 209 projection (in the general market range for games involving low scoring Indiana this season).

Betting Over/Unders isn’t as easy as it might seem…particularly if you’re a novice prone to knee-jerk reactions.

The ultimate guide to predictive college basketball analytics

You’re smarter than the typical college basketball fan. You hear about sports analytics and want to know more about how it applies to college basketball.

Maybe it’s November, and you’re looking to bet a few games as states continue to legalize sports betting. Maybe it’s March, and you need an edge in winning your NCAA tournament pool.

This article covers the basic concepts in college basketball analytics such as points per possession and the four factors. It does so by asking two questions:

  • How do you make a prediction for a game?
  • Do matchups matter in predicting a game?

The article ends with a curated list of further reading. This list covers the randomness of the three point shot, ensemble methods for making predictions and how to win your March Madness pool

Let’s dig in.

1. How to make predictions based on points per possession

Let’s suppose Gonzaga and Michigan State face off in the NCAA tournament championship game. You want to make a prediction for this game based on data.

The first thing to consider in making a prediction is the pace of play. Gonzaga likes to get up and down the floor, while Michigan State prefers the half court game.

Based on this style difference, it doesn’t make sense to look at points per game. For a more accurate estimate of team strength that considers pace, the analytics community looks at points per possession.

While it’s trivial to get the points in a game, the box score does not provide the number of possessions. Let’s look at how to estimate this number.

1a. Estimating possessions

To estimate the number of possessions in the game from the box score, let’s consider how a possession can end.

One way for a possession to end is a turnover (TO). The box score tracks each turnover, so each turnover counts as one possession.

Next, consider possessions with a field goal attempt (FGA). A possession ends after a field goal attempt in two situations:

  • the offense makes the shot
  • the offense misses the shot and the defense gets the rebound

The possession could get extended if the offense grabs the rebound (OREB). To account for these 3 situations, we estimate possessions with a field goal attempt by FGA – OREB. Offensive rebounds get subtracted because the offense can take two shots on one possession if they get an offensive rebound.

A possessions can also end with free throws. If every free throw came in a pair because of a shooting foul, the number of possessions would be half the free throw attempts (FTA). Unfortunately that’s not always the case, as the offense can:

  • get one free throw after a made basket
  • get three free throws when fouled on a three pointer
  • miss the front end of a one-and-one

Instead of a half, you need a different factor that accounts for these situations. Ken Pomeroy uses 0.475, and I use this factor in my calculations as well.

To estimate possessions (POSS) from the box score:

POSS = FGA – OREB + TO + (0.475 * FTA)

In college basketball, teams could differ in the number of possessions by two if one team gets an extra possession each half. To estimate the possessions in one game, you apply this a formula to both teams and take the average.

During the 2019-20 season, college basketball teams averaged about 70 possessions per game. Gonzaga was one of the faster teams, as they averaged 74 possessions per game. Michigan State played an average pace of 70 possessions.

It’s possible to get a more accurate possession count through play by play data. This also presents an interesting opportunity to eliminate possessions at the end of games with intentional fouls, as they don’t reflect the normal flow of play.

1b. Ranking college basketball teams

To evaluate college basketball teams on offense and defense, we use points per possession as an efficiency metric. The last section showed how to estimate possessions from the box score.

During the 2019-20 season, college basketball teams averaged 100.5 points per 100 possessions. The offense gets about a point each time they have the ball.

To get from points per possession to college basketball rankings, you need to adjust for strength of schedule. College basketball has a wide range of teams, and every fan recognizes the difference in playing Michigan State versus Mississippi Valley State.

There are many ways to adjust for strength of schedule. Here, I’ll focus on the methods of Ken Pomeroy, as his college basketball rankings are the most widely known.

To adjust for strength of schedule, Pomeroy uses the least squares method. This is also the basic idea behind linear regression, the data science technique most often used to find the correlation between two variables. For a visual primer on regression, click here.

This least squares method also drives the team rankings on the Sports Reference sites. They call it the Simple Rating System (SRS), and this method assigns a rating to each team in college basketball. The difference in the rating between two teams gives a prediction for a future game.

To perform this calculation, the computer changes the ratings for all 353 teams until the ratings meet a criteria. This criteria is that these ratings minimize the error between the prediction from the ratings and actual game results.

Pomeroy takes it one step further as he considers offense and defense for each college basketball team. Instead of 353 variables, his code changes 706 variables to minimize the error to the efficiency by points per possession in games.

Since these variables get solved for simultaneously, the offensive rating for Gonzaga depends on 705 other offensive and defensive ratings. Michigan State’s defensive rating matters to Gonzaga’s offense, even if Gonzaga and Michigan State have yet to play.

In his calculations, Pomeroy puts more weight on recent games. After performing these least squares calculation, you get the offensive and defensive rankings on kenpom.com. These two numbers get combined into his team rankings.

Now let’s see how these adjusted efficiency numbers imply a prediction for a game.

1c. Making a prediction

With offensive and defensive ratings based on points per possession, we can now make predictions for games. Let’s use Gonzaga against Michigan State as an example.

First, consider what the offensive and defensive ratings mean. For example, if Gonzaga has a rating of 115 points per 100 possessions, then they are expected to score 115 points per 100 possessions against an average college basketball defense.

As another example, Michigan State might have a defensive rating of 90 points per 100 possessions. This means that they’re expected to allow 90 points per 100 possessions against an average college basketball offense.

To make a prediction between Gonzaga’s offense and Michigan State’s defense, you have to consider that Michigan State’s defense is much better than average.

To do this, consider the deviation of a team’s rating from average. To simplify the math, let’s use an average efficiency of 100 points per 100 possessions.

Gonzaga’s offense is 15 points better than college basketball average, but Michigan State’s defense is 10 points better than average, both per 100 possessions. Better defenses have lower ratings.

A common way to make a prediction is that Gonzaga’s offense will score 5 points per 100 possessions better than average. This is because 15 (Gonzaga’s deviation from average on offense) minus 10 (Michigan State’s deviation from average on defense) is 5. This is the same method I use with yards per play and success rate in football predictions at The Power Rank.

Gonzaga is predicted to score 105 points per 100 possessions against Michigan State. If you scale this efficiency to 70 possessions for a game, this implies Gonzaga will score 73.5 points.

You can do same calculation for the other matchup. Suppose Michigan State’s offense has a rating of 111 while Gonzaga’s defense has rating of 93 (both measured by points per 100 possessions). You can work out that Michigan State’s offense is predicted to be 4 points better per 100 possessions. This implies 72.8 points in a game with 70 possessions.

Based on these hypothetical numbers, Gonzaga would be predicted to win by 0. 7 points.

While I have assumed 70 possessions in this game, you could assume a different number, especially if Gonzaga plays faster than average. With this method, there is clear freedom to adjust for pace.

Get my March Madness cheat sheet

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2. Do matchups matter?

During the 2019-20 season, West Virginia was an elite offensive rebounding team. In contrast, Texas was an awful at defensive rebounding, worst in the Big 12.

When West Virginia plays Texas, do they have an edge due to this matchup? Can we use this to make a better prediction?

Jordan Sperber of Hoop Vision has done excellent work on matchups. To understand his results, let’s look at the four factors of basketball, which provides a quantitative method to look at matchups.

2a. Four factors

Dean Oliver was a pioneer in basketball analytics. In 2003, he first published his book Basketball on Paper that laid the groundwork for future work in basketball analytics.

In the book, he wondered what factors made an offense great. Shooting is an obvious asset, but what else matters? Oliver wrote down four factors:

  1. shooting
  2. offensive rebounding
  3. turnovers
  4. getting to the foul line

Let’s examine these four factors in more detail and how to define a rate statistic for each.

The first of the four factors is shooting, as an offense can’t score without making baskets. The most simple measure of shooting is field goal percentage, or field goals made divided by field goal attempts.

A better formula for shooting gives the offense more credit for a three point shot. Effective field goal percentage assigns this extra 50% credit for a three. In college basketball, the average effective field goal percentage is about 50%.

The second factor is offensive rebounding, as the offense keeps a possession alive with an offensive rebound. Total offensive rebounds is not a good measure though, as this depends on the shooting accuracy of the opponent.

Instead, consider the offensive rebounding rate, or the fraction of rebounds the offense gets on that end of the court. This offensive rebounding rate is offensive rebounds divided by the sum of offensive rebounds plus the opponent’s defensive rebounds.

In college basketball, the average offensive rebounding rate is about 28%. Since the defense grabs the other rebounds, the defensive rebounding rate is 1 minus the opponent’s offensive rebounding rate.

The third factors is turnovers. A team can’t score if they commit a turnover before taking a shot.

To measure turnovers, consider turnover rate, or turnovers divided by possessions as estimated from the box score. On average, college basketball teams turn over the ball on about 19% of possessions.

The final factor is getting to the foul line. Since college basketball averages a 70% free throws percentage, taking two free throw attempts is an efficient means to score points.

To measuring getting to the foul line, one metric is free throw attempts divided by field goal attempts. In college basketball, this rate is about 32%.

It’s also reasonable to define this factor as free throw made divided by field goal attempts. This definition includes the ability to make free throws in addition to getting to the foul line. However, I’ll use free throw attempts to isolate the ability to get to the foul line.

Oliver’s four factors explain offensive efficiency, or points per possession, almost exactly. To explain this, I’ve run a linear regression on the four factors to explain points per possession on the team level. This process assigns a weight to each of the four factors.

When you do this analysis for college basketball, the four factors explain 98% of the variance in offensive efficiency.

Based on this regression analysis, which of the four factors is the most important? Shooting is the most important of the four factors, not any kind of surprise. Offensive rebounding and turnovers have about the same importance but less than shooting. The least important factor is getting to the foul line.

2b. Extremes in matchups

With these four factors, let’s get back into the question of whether matchups matter in making predictions. In 2013, Jordan Sperber wondered whether a team that excelled in one of the four factors would have an advantage over an opponent weak in the opposing factor.

In particular, can you make a better predictions based on West Virginia’s excellence in offensive rebounding and Texas’s weakness at defensive rebounding?

To study this, Sperber isolated games in which teams had extremes in rebounding. He defined an extreme as a team in the top or bottom 10% in offensive or defensive rebounding rate.

With both elite and awful units, there are four types of games:

  • an elite offense versus an elite defense
  • an elite offense versus an awful defense
  • an awful offense versus an elite defense
  • an awful offense versus an awful defense.

Sperber isolated games with these matchups and asked how well adjusted offensive and defensive efficiency can make a prediction in each game, as discussed in the previous section. He compared this prediction with the actual efficiency in the game.

For example, his data set had 311 games with an elite offensive rebounding team versus an awful defensive rebounding team. In looking at the efficiency prediction versus what actually game efficiency, the average difference was less than a point per 100 possessions.

The prediction based on offensive and defensive efficiency was able to explain the outcome of these games. Here is the main result from his study: Sperber found the same predictive accuracy in all four types of matchups.

He repeated the study on the other three factors and found the same result. The efficiency prediction was equally accurate in each of the four types of matchups.

Here is the take home message: team level matchups in the four factors do not help in predicting the outcome of a college basketball game. In some sense, the matchup is already considered in the efficiency numbers. You can see this from how well the four factors explain efficiency.

Don’t extrapolate these results too far. If your team plays a six foot three inch center, he’s probably going to get killed by Joel Embiid. However, based on the four factors, matchups do not help you make better predictions. Offensive and defensive efficiency by adjusted points per possession does an excellent job.

3. Further reading

Need more college basketball analytics? Check out these resources for further reading.

3a. The three point shot

The three point shot is a powerful weapon. It gives the underdog an opportunity to get hot and pull off the upset. It has also propelled a favorite like Villanova to two NCAA tournament championships.

Ken Pomeroy wondered whether the offense or defense has control over the three point shot. To study this, he looked at the correlation from early to late season statistics in conference.

He found that the defense has the ability to control what type of shots an opposing offense takes. Defenses can limit the fraction of shots an opponent takes from three.

However, the defense has no control once the offense puts up a three point shot. Randomness plays a big role in determining three point percentage allowed.

Even more surprising, randomness also plays a big role in an offense’s three point percentage. While shooting is a clear skill, the data shows regression to the mean in three point percentage.

To read Pomeroy’s article on the three point lottery, click here.

I’ve also looked at the randomness of three point shooting for NBA players. To check out that study, click here.

3b. Ensemble methods to predict the tournament

Nate Silver has published predictions for the NCAA tournament at both the New York Times and his own site FiveThirtyEight. The key to accurate predictions are ensemble methods that combine many predictors.

First, Silver combines 6 different power ratings to get an estimate of team strength. Each system has its weaknesses, but the combination provides a powerful predictor.

In addition, Silver adds an unexpected component: the preseason AP poll. It might seem strange to add a predictor that has no access to data from the current season.

However, the preseason AP poll is a powerful predictor of tournament performance that harnesses the wisdom of crowds. No one sports writer submits the perfect ballot, but the combination of many sports writers gives an accurate assessment of team strength.

In fact, my article on FiveThirtyEight has shown that the preseason polls are a better predictor than the RPI, the outdated computational method the selection committee previously used to seed the field.

An earlier article on these NCAA tournament predictions inspired the ensemble approach I use for my member predictions at The Power Rank. This includes college football and the NFL in addition to college basketball.

To check out the methods behind Nate Silver’s NCAA tournament predictions, click here.

3c. How to win your March Madness pool

Armed with analytics and win probabilities, you’re ready to win your March Madness pool. However, you should not simply pick the higher ranked team in every game.

This favorites strategy gives you the highest win probability for small pools. But in some years, there is a more optimal strategy for intermediate sized pools.

Sometimes, the public gets overexcited about a team, such as the 2015 Kentucky team that entered the tournament undefeated. As the numbers suggest, suppose you also pick this team as champion.

If this favorite wins, you and many others will get the 32 points for picking the correct champion. With so many other people in contention, it’s likely someone will get lucky in earlier rounds and beat you.

Instead, you should make a contrarian choice of a different team with a high win probability but not getting picked in many pools. If this team wins, you have a great chance to win your pool.

I explain these ideas in my book How to Win Your NCAA Tournament Pool. I’ve posted the Introduction here at The Power Rank. To check out the entire book for less than the cost of a latte, click here.

Get my March Madness cheat sheet

At The Power Rank, I use data and analytics to make better football and March Madness predictions.

If you sign up for my free email newsletter, you’ll get:

  • my March Madness cheat sheet that makes it drop dead easy to fill out your bracket
  • a sample of my best football and college basketball predictions usually saved for paying members of The Power Rank
  • updates on content like this guide to college basketball analytics

To sign up, enter your best email and click on “Sign up now!”

Basketball Total Betting: Strategies and Calculation Rules

Basketball is one of the most popular sports for betting. Among the fans of this game, such a variant of the market as total is in special demand. In our material, we will tell you what types of this market are, analyze how this type of bet is calculated, and also talk about existing strategies and give some useful tips that will help you make successful bets on total in basketball.

Total in basketball - a type of bet that implies a bet on the number of certain units in a particular match. Bookmakers offer a wide range of options for this market. You can bet not only on the total number of points in the match, but also on the number of three-pointers, free throws, assists, rebounds, an individual indicator of any of the parties or a specific player, as well as on the total of a separate quarter or half of the match.

Most often, this bet is indicated in the line as TB and TM, where total is the number of units in the event, and "b" and "m" are abbreviations for the words "over" and "under". Accordingly, the player bets on a greater or lesser number of units of the value that the BC sets.

Types of totals in basketball

Total points in a match (classic)

In this type of total, the bettor seeks to predict whether the teams will exceed the total number of points offered by the bookmaker. For example, take the NBA championship game between Brooklyn and Cleveland. Let's make a bet in the amount of 100 USD. on TO 220.5 points including overtime.

Thus, to win, the expected number of points scored by both sides must be 221 or more. Those. a conditional score of 115:106 will suit us, since 115+106=221. If you win, multiply the bet amount by the coefficient and get 90 c.u. net profit. A result in which the teams score less than 220 points in total will result in a loss of money.

Individual total

Type of bet in which the bettor tries to predict the number of points of one of the meeting opponents. Let's look at the example of the match between Golden State and Detroit.

Suppose we bet $100. on an individual Golden State total over 117. 5. In this case, in order to win the bet, we need Golden State to score over 117 points at the end of the meeting. At the same time, the number of points scored by both sides or the total points of Detroit do not affect the success of the bet. The mathematics of calculating profit is similar - the amount of the bet is 100 USD. multiply by quote 1.98, we get 98 c.u. net gain.

Bet on the total of the most productive/non-successful quarter

When playing this type of total, the bettor needs to predict the scores based on the results of the most productive or non-successful quarter of the match.

We will put 100 c.u. on the total of the highest scoring quarter over 61.5 in the match "San Antonio" - "Phoenix". The most productive segment of the meeting was the third quarter, its total - 63 points. This is more than the number of 61.5 points offered by the bookmaker, so the bet won.

Total even/odd

The most risky variant of this type of market. By placing a bet on such a market, the bettor is trying to predict whether the total score of the match or quarter is even or odd.

Let's assume that we bet on an even total for the match, including overtime. This means that any result that will be divisible by two without a remainder will suit us. For example, 116:108, which in total gives an even total of 224.

The result, where the teams score together a number of points that is not a multiple of two, will mean the loss of the bet.

Strategies for betting on totals

Shchukin's strategy

A strategy based on purely statistical indicators of teams in recent meetings. In order to calculate the estimated total of the upcoming match, you need to perform several actions:

  1. We add up the individual scores of the hosts of the future match in the last five games. We divide the resulting number by five, after which we add one point for each victory and subtract 1.5 points for each defeat. At the end, we add three points for the status of the host team.
  2. We do the same arithmetic operations for the visiting team, but do not add the points assigned for the host status.
  3. We calculate the individual scores of teams in the last five head-to-head games and divide it by five.
  4. Add up the values ​​obtained for each side in the first two and in the third paragraphs. We divide these numbers by two. We add up the received indicator of each of the rivals, as a result of which we find out the expected total of the match.
  5. We compare the calculated indicator with the weighted average value (the main total with approximately equal odds for Under and Over), which is set by the bookmaker. If the figure we received is five points higher than the bookmaker's value, we bet on TB, if it is five or more points lower, we play TM.

Let's analyze Shchukin's strategy on the example of a real match "Sacramento" - "Houston".

We calculate the average total of the last matches of Sacramento:

(126+125+108+88+111)/5+3+(1+1-1.5-1.5-1.5)=111.7

We perform the same computational operations for Houston:

(114+128+91+123+106)/5+(1-1. 5-1.5-1.5-1.5)=114.3

Calculate the average total in the last five head-to-head matches:

Sacramento: (126+125+94+119+112)/5=115.2

Houston: (114+105+102+122+129)/5=114.4

We add up the obtained indicators for each of the teams:

Sacramento: (111.7+115.2)/2=113.45

Houston: (114.3+114.4)/2=114.35

We get the expected total: 113.45+114.35=227.8

The bookmaker's weighted average total is 238.5 (over and under at 1.90), which exceeds our result by more than 5 units. Accordingly, the match is suitable for betting on TM 238.5.

The meeting ended with the score 112:118. The total total is 230 points, the bet has played.

The disadvantage of this strategy lies in its reliance solely on simple statistics without taking into account other important factors: the level of previous opponents, the calendar, the shape of the leading players.

Total control

The strategy involves playing live. A few hours before the start of the meeting, we find the weighted average total in the line, fix the set quote. After the first half (two quarters) of the game, we compare the current coefficient for the selected indicator with the pre-match one.

If the quote has changed by 10% or more, we place a bet in the opposite direction. That is, if the odds on TB have increased from about 1.95 to 2.14, we bet on TM. We do the same with the reverse movement of the quote - with an increase on TM, we bet on TB.

Total based on the line change

The essence of this strategy also lies in the analysis of betting values. 10-12 hours before the start of the match, we find an equiprobable total in the line. Two or three hours before the fight, we again look at the line and analyze the changes according to the weighted average.

For example, the weighted average total in the morning line is 202, the bookmaker estimates the probability of Under and Over at 1.90. Before the start of the meeting, we evaluate changes in the equiprobable indicator. If it has grown to 204, then the coefficient for the total less than 202 has increased, and for the total more it has fallen. In this case, we take TM 202, since the bookmaker is simply trying to balance cash flows in order to avoid losses. The strategy also applies in the opposite direction. If the equiprobable total fell to 200, for example, we take over 202 with an increased coefficient.

Corridor betting system

The essence is to simultaneously bet in different offices, trying to get into the so-called "corridors" between the values ​​in two bookmakers. For example, in one bookmaker we bet on TM 170.5, and in another on TB 167.5. If we manage to take both markets with odds greater than 2, we will be in the black even if one bet passes. If the total points in the match fall into the “corridor” from 167.5 to 170.5, both bets will win.

Corridors are rare, but they can still be found. This will require more time for a detailed analysis of the line of several bookmakers.

Average number strategy

Strategy for calculating the probability of passing an individual total. The average total during the season for the selected team is calculated - the total number of points in all matches played is divided by the number of games.

The point is to bet in the opposite direction after several matches with an increase or decrease in the average value, since according to the theory of averages, the team will sooner or later roll back to its usual indicator.

Golden State's average point total fluctuates around 115 points throughout the season. As soon as we see that the individual indicator in 2-3 matches in a row is below the average mark, this gives a reason to bet on ITB in the next meeting.

The example below illustrates this pattern. After two games of 104 and 105 points, Golden State are back to their average totals of 119,116 and 118 for the next three games.0011

It should be remembered that this or that strategy for the total in basketball is not one hundred percent winning. Any statistical technique should be combined with a detailed pre-match analysis and a detailed study of the news background.

You need to know the rules of the sport you plan to bet on thoroughly. For example, in North American and a number of Asian leagues, a quarter is played for 12 minutes, i.e. match time is 48 minutes. In most European championships, the playing time of the quarter is ten minutes, respectively, the meeting lasts 40 minutes of pure time.

Learn the rules for accepting and calculating bets at your bookmaker. As a rule, bets on total are calculated taking into account possible overtime. However, there may be exceptions, so you should pay attention to such nuances.

Follow your chosen sport, study statistics and trends. The NBA championship is traditionally famous for its scoring matches, however, there are clubs there that demonstrate a low average performance during the season.

When analyzing matches, take into account the motivation of opponents, as well as the absence or presence of key players in the squads. The density of the match schedule can have a great influence on the physical condition of athletes, so it is important to study the team schedule.

Do not focus on any one strategy, combine different options. This will help level the disadvantages of a separate theory and approach the analysis of events more comprehensively.

Conclusion

As you can see, bookmakers offer a wide range of bets on total basketball. This type of market can be very profitable, but for a successful game it is necessary to observe financial discipline, as well as to correctly use existing strategies.

Bets on total in basketball

  • Strategy for betting on totals in basketball
  • Even/odd quarters
  • Individual total for the most successful players
  • TB for meeting strong teams
  • Bets on total quarters
  • take into account when predicting the total in basketball

Bets on the total in basketball are one of the most popular types of betting within the discipline. This is due to the fact that the forecast is relatively easy to make, and the odds are slightly higher than for the outcome. Today we’ll talk about how to effectively bet on basketball for total.

Strategy for betting on totals in basketball

There are many strategies for betting on totals in basketball. Consider the most popular and effective.

Even/Odd for quarters

Even/Odd is one of the varieties of forecast for total. The task of the better is to guess whether the team (or both opponents as a whole) will score an even or odd number of points.

The strategy is a kind of catch-up and is based on the mathematical theory of probability. Game plan looks like this:

  • the better matches multiple matches;
  • then the player bets on even/odd in a single quarter of the first meeting;
  • regardless of the results of the first bet, he bets the same amount on the same result in the same quarter of another match.

Although the strategy at first glance seems spontaneous and unreasonable, it is bearing fruit. The fact is that according to statistics, 2 quarters out of 4 end with an odd score of 1 of the teams. Thus, if a better bet on 4 matches, he will win with a probability of 50% (or a little less - here you need to take into account the element of surprise when choosing a game).

As you can see, even/odd in a quarter is a clean lottery. But no, not a single match, in the quarters of which there would be only an even TOTAL or vice versa

Let's analyze the strategy using examples. Let's take 3 NBA games of the 2019/2020 season:

  • Miami Heat - Milwaukee Bucks on September 09, 2020. The quarterly count is: 19:28, 33:18, 21:19, 30:29.
  • Boston Celtics - Toronto Raptors dated September 12, 2020. The quarter count looks like this: 26:27, 24:19, 22:25, 20:16.
  • New Orleans Pelicans - Orlando Magic dated August 14, 2020. The quarterly score is as follows: 31:34, 30:43, 26:22, 40:34.

On the examples given, you can see that the even and odd scores for each team keep the proportions of 50 to 50 and practically do not deviate from them. And if they deviate, it is quite insignificant. A similar pattern can be traced in about 95% of NBA games.

If you choose the right coefficients, the income will be guaranteed. Multipliers must be 2.1 or higher.

Individual total for the most successful players

An effective strategy for betting on total in basketball. The logic here is that each team has several "stars" who play more efficiently than the rest of the athletes. If we analyze several games of the season (usually 10-15 are enough), we can calculate the arithmetic average of points scored by such basketball players. In 85% - 90% of matches they won't score less. Accordingly, based on the resulting number, you can bet on the total more.

Let's analyze this pattern on a specific example. The leading player of Los Angeles Lakers is LeBron James. This is a truly "star" basketball player, bringing good results to the team. Compared to other athletes, James scores the most points per game. The average for this is 27.6. Thus, you can safely bet on an individual total over 25 on a basketball player.0003

Let's take the Los Angeles Lakers vs Miami Heat game on October 12, 2020 as an example. As almost always, James showed the best result in the team. The athlete scored a total of 28 points. Thus, TB >25 would play here. A similar prediction would have been effective in most other LeBron James games.

TB to meet strong teams

If two strong teams enter the court, scoring 100 or more points per game, it makes sense to bet on TB>200. With an active attack from both sides, this number will certainly be reached.

Toronto Raptors is a strong team. Almost all team matches end with a score of at least 102 - 104 points. It remains to find her a worthy opponent in the NBA. The Boston Celtics can claim this role, having almost identical results.

Now let's look at the results of the match Toronto Raptors - Boston Celtics, held on September 10, 2020. The final score was 125:122 . Thus, over 200 would play here for a coefficient of 1.98

Bets on total quarters

There are certain stereotypes of team behavior in quarters in basketball. Perhaps two main scenarios.

  1. The first – the outsider shows himself clearly in the first quarter, then loses strength and loses in the remaining three.
  2. The second - the weak team gathers by the end of the meeting and wins in the fourth quarter.

Which scenario will be implemented in a particular game, you need to look at the statistics. Different teams behave differently. Someone almost always actively plays in the first quarter, someone in most cases wins in the fourth.

For equal opponents, the odds for TOTAL in quarters are approximately the same. But in live you can catch good quotes.

Similar patterns can be used for betting on quarter totals in basketball.

The main advantage of betting on totals in basketball is the relative ease of forecasting. If you correctly approach the analysis of the game, it will not be difficult to predict its outcome.

The main disadvantage of this type of bet is that bookmakers offer low odds for more or less obvious outcomes.

Yes, here the multipliers are higher than for the outcome. But values ​​in 1.5 - 1. 6 almost never exceed.

However, the disadvantage is compensated by the low risk of losing the bank. After all, it is not so difficult to predict the final number of points - the result is almost always obvious.

What to consider when predicting the total in basketball

When preparing a prediction for a bet on the total in basketball, you need to consider the following

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