# Sports Betting 101

What the Pointspread Represents

What it takes to win

Money Management

Handicapping Theory

Technical Analysis

**What the Pointspread Represents:**

Before engaging in methods to help you beat the pointspread, it is important that you understand the pointspread and what it represents. Most people view the pointspread (or line) as the predicted margin of victory of one team over another. In fact, that is not the case. The job of an oddsmaker is to get half of the wagered money on one side and the other half wagered on the other side. By splitting the bets, with 50% of the money being wagered on each team, the house is guaranteed of making money. The odds in sports betting wagers that utilize a pointspread are 11 to 10, meaning that a gambler must risk $11 on a losing bet to profit $10 for a winning wager. In other words, there is an extra 10% that must be paid on losing bets. Thus, if the oddsmaker sets a pointspread that splits the money, he uses the loser's money to pay off the winners and has an extra 10% of the loser's money as a profit. So, the job of the oddsmaker is not to predict the margin of victory between two teams and use that as the pointspread, but instead to set a pointspread that will split the money wagered on a game. To do this, the oddsmaker is really gauging public perception. So, in essence, the betting public sets the line. For example, if Team A is 4 points better than Team B, but the public thinks that Team A is 6 points better than Team B, then the pointspread is more likely to be closer to 6 points (public perception) than it will be to 4 points (the realistic difference between the teams). So, the job of a handicapper is not to know more than the oddsmakers, but to know more than the general betting public. Given that, our task doesn't seem nearly as daunting.

What it takes to win:

What it takes to win:

The large majority of people that wager on sporting events believe that they are going to win, but the fact is that the large majority of sports betters lose in the long run. In fact, the average sports bettor wins at a rate of 48%, which is worse than if they had just flipped a coin. So what does it take to beat the break even mark of 52.4% (you need to win 11 bets for every 10 you lose and 11 divided by 21 is equal to .524)? To win in the arena of sports betting you must employ a disciplined approach in your analysis of each game using methods that have proven to be successful in the long run. I'll get to some of the techniques that I use in handicapping later in this series of essays, but you must realize that even the best and most knowledgeable handicappers win at a rate of about 57% to 60% in the long run. From talking to sports bettors over my 19 years as a professional handicapper I have realized a few misperceptions that exist in the minds of many of them. First, there are many sports bettors that think winning 60% of their bets is easy and that they can do even better than that. If they can, that's great. But more than likely, those that think that they win at least 60% of their bets are suffering from selective memory, which entails remembering their great winning weeks while conveniently forgetting about the losses. I suggest keeping a log of all bets made and getting a realistic idea of what kind of handicapper you are. The other popular misconception is that winning 58% of your bets will not make you much money. On the contrary, 58% winners at 11 to 10 odds will make you plenty of profit over the course of the year. I will demonstrate that point in the money management section below.

**Money Management:**

This money management section is for those of you that have some sort of logical method for picking the games that you are wagering on and are looking for a strategy to bet those games that will result in a profitable season. First off, if you pick more losers than winners, no money management system is going to lead you to make a profit in the long run. Later in this series of articles, I will discuss some of my handicapping methods that have resulted in consistent, winning results in my 12 years as a professional handicapper. Some of you do your own handicapping and make a list of games that you consider to be good bets. Others pay for picks from services such as mine that have proven to be winners over the years. Either way is fine and I suggest making a list of the sides that you are going to wager on for the weekend and how much each wager will be. Once you have that list, stick to it and don't deviate from it based on how well or poorly you are doing as the weekend unfolds. How your bets did on Saturday morning should have no influence on the games you've chosen to bet on Saturday night, or the amounts of your bets. In my years as a professional handicapper, I have come across a number of clients that will find ways to lose money even when I am having a very profitable season with the selections that I give them. Their problem, and one of the biggest problems with most sports bettors, is money management. Before outlining the money management strategy that I recommend, I will first point out some of the problems that I've seen over and over in my 12 years in this business. First off, the key to any good sports bettor is discipline. Discipline is the key to picking winners in the first place and implementing a disciplined money management system is key in translating those winning selections into a profitable season. The problem with many sports bettors is that they lack discipline in both the games that they choose to bet and the amounts that they are betting.

I can't tell you how many times I've had a decent winning week on my Best Bets only to hear on Monday from some clients that they lost money. The problem was not the games that I recommended betting, but rather it was the way that they bet them. Here's an example. On Saturday I give out 5 Best Bets that are all rated that same, 3 of which are in the morning and 2 at night. One client wagers $200 each on the 3 morning Best Bets and all 3 of them are winners. This client, feeling like I'm hot, doubles his bets for the 2 night games, wagering $400 on each only to have both of them lose. Another client with the same bankroll plays all 5 games at $200 each, as he should. Both clients won 3 plays while losing 2, but the first client is down $280 (wins $600 in the morning and loses $880 by doubling up on that evening's two plays), while the second client is up $160. On Sunday I give out 4 morning games as Best Bets and 3 afternoon games plus the Sunday night game is a Best Bet (let's assume that all of the Best Bets have the same rating). The first client goes back to his $200 per game wagers and plays my 4 morning games while the second client does the same, betting $200 on all 4 of the Best Bets. Unfortunately, those Best Bets go 1-3 and both clients are down $460 in the morning. I have now lost 5 of my last 6 games, and the first client (now down $740) backs off and decides not to bet my 3 afternoon games. The second client (down $300) bets all 3 games at $200 each, sticking to his plan. All 3 afternoon Best Bets are winners and I head into the Sunday night Best Bet with a respectable 7-5 Best Bet record so far for the weekend (3-2 on Saturday and 4-3 so far on Sunday). The first client (still down $740) decides to jump back in and bet my Sunday night Best Bet for his standard $200 and the second client (now up $300) once again wagers $200 on that Best Bet. The Sunday night Best Bet is a winner and I am now 8-5 for the weekend on my Best Bets following Sunday's action. However, the first client is just 5-5 on those Best Bets because he was afraid of betting the 3 Sunday afternoon Best Bets after losing in the morning. Actually, 5-5 is no disaster but because of doubling up on his bets for the 2 losses on Saturday night, he is down $540. The second client bet the same on each and every Best Bet I released and he is now up $500 for the weekend. As it turns out, I also have a Monday night Best Bet. The first client realizes that I have no won 4 straight NFL Best Bets and this is his last chance to get even for the weekend, so he bets to win $550 (risking $605). The second client also realizes that I've won 4 straight Best Bets, but he sticks to the plan and wagers his standard $200. The Monday night game turns out to be a losing bet and the first client ends the weekend down $1145 while the second client, betting $200 on each and every Best Bet, is up a modest $280 on my Best Bet record of 8-6 (57%) for the weekend. There are a few lessons to be learned from the actions of the first client. His first mistake was raising his betting amount from $200 a game to $400 a game after my 3-0 start on Saturday morning. My winning those 3 games does not change the chance of winning either of the 2 Saturday night Best Bets, just as losing all 3 of those games would have had no effect on the later Best Bets. The second mistake was that he let fear interfere with his thinking after losing 3 of 4 Best Bets on Sunday morning (and now 5 of 6 going back to Saturday night). This client either thought that my recent losses had some effect on the Sunday afternoon Best Bets or he was afraid to lose any more money that weekend. If the latter was the case, then he shouldn't have been betting as much per game as he was (I'll approach the subject of amount to bet per game in a bit). And, of course, the final mistake is probably the most common. The first client bet almost 3 times his normal amount on the Monday night Best Bet in a effort to make up for his losses. Never bet on a game to bail yourself out of a hole, just accept the fact that you had a losing week and move on to the next week. Losing is part of the process of winning and the most successful sports bettors handle losing weeks in stride and move on to the next week without changing their time-tested method of handicapping. Also, never bet extra when you are up to try to make a big score. Like I said before, the percentage of games won up to that point in the weekend has no bearing on the chance of winning your next game and it is silly to raise your betting amount because you feel “hot". The point here is not to let greed or fear interfere with your decision making. Your best decisions are made prior to the start of the weekend, so decide which games you are going to bet prior to the start of the weekend and stick to that list. Decisions made during the course of the weekend are to often influenced by results up to that point and fear and greed can get in the way of making good decisions. Prepare for the weekend by doing work during the week to isolate the Best Bets. If you are not good at deciding which games are good bets and you still insist on betting sports, then seek the help from an honest source with a proven track record (consider my Best Bets available on this site).

Let's now lay the framework for a money management system.

**1) Establish a bankroll:
**Most money management systems tell you that must have a bankroll set aside for the sole purpose of wagering on sports and that this money should not be needed for any other purpose (like paying the rent, putting your kids through school, etc.). While having money set aside solely for the purpose of betting is a concept that I agree with, many sports betters don't have the luxury of having money that they can set aside for betting only purposes.

**My advice to you is to find an amount that you can afford to lose over the course of a season that would still enable you to live the lifestyle you wish to live without stressing too much about it.**In other words, if losing $10,000 over the course of a season will put you in a serious bind, then find an amount that you could lose and still live your life without too much stress. Nobody is immune to being in the hole, no matter how good of a handicapper they are. Prior to starting a sports service, I had gambled my way through college my final 3 years. In the first two years, I was never seriously in the hole at any point in the season (I started one season with 5 straight winning weeks and the next season with 3 straight winning weeks) and had won 64% of my bets in football those first two years. I never imagined that I could have a losing month let alone a losing year. That third year was different, however. I started the season with just one winning week in my first 7 weeks and had a record on my bets of 11-18. I owed my roommate rent money and tuition was due for the next semester. I had to borrow money from my dad to bail me out (I remember him being pretty pissed off). Due to the many hours of research that I did and my consistent success on my wagers in my first 2 years betting, I thought I was immune to losing and I was betting more per game than I could afford, figuring each week was the week that I was going to win. That season did turn around, as I went 31-9 over the course of the rest of the season to finish at 42-27 (61%). I will use that year as an example of my money management strategy at the end of this section, but the point here is that I learned that even good handicappers can get in the hole. Therefore, it is important to know how much you can realistically afford to lose without getting yourself or your family in financial trouble. Once you have come up with that amount, you can consider that to be your maximum bankroll. For those of you that do have money that you can set aside to wager, pick an amount that you wish to invest knowing that it is possible to lose most of that amount. Now that you have established a bankroll, we can move on.

**2) Set your expectations:**

Once a bankroll is established, the next step is to set realistic expectations on the number of wagers that you will make and on the percentage of those wagers you expect to win. The idea is to use that information to determine the amount to be wagered on each game that will give you nearly no chance of exhausting your bankroll (actually, I use less than a 0.2% chance of going broke). It is always best to be conservative in estimating the percentage of winners. You may have hit 62% last year, but that is an unrealistic expectation unless that is a percentage that you've been winning for years. For most of you, I suggest setting your expected percentage of winners closer to 50%. By doing so, you will give yourself even less of a chance of every hitting bottom during the course of a season. If you have kept good records over at least the last 4 seasons and have established a winning percentage, then you can certainly use that percentage when calculating your average bet to be made during the season. The number of wagers that you expect to make is an important part of any money management strategy since the more games that are to be played, the higher the total variance will be. For those with a statistics background, I will quickly explain the method I used to determine the amount to play on each game. There are two meaningful results when betting on a game with a pointspread, a winning bet and a losing bet (I will ignore a push since no money is won or lost). The distribution of wins and losses is a binomial distribution with a win equal to 1 and a loss equal to 0. Let P equal the percent chance of a victory expected with each wager and N equal to the number of games to be wagered. For the purposes of this example, let's assume that the percentage of wins expected (P) is equal to .50 (or 50%). Each wager made has an expected value of .50 plus or minus .50 (you either win or lose). In general, the number of wins expected in N wagers is P x N with a variance equal to the square root of P x (1-P) x N. If P were equal to .50 and N were equal to 100 wagers then our expected amount of wins would be 50 (.50 x 100) with a variance of 5 (.5 x .5 x 100 = 25 and the square root of 25 equals 5). As the number of wagers (N) gets bigger, the distribution of results, while still binomial, more closely resembles what is called a normal distribution. In a normal distribution, the chance that the result of a sample is 3 or more times the variance away from the expected value is 0.3%. In our example, 100 wagers that each have a 50% chance of winning, we are 99.7% confident that the number of wins will be 50 plus or minus 15 (3 times the variance of 5). In money management, we are concerned with not exhausting our bankroll, so it is the 50 minus 15 that we are interested in. In this example, there is only a 0.15% chance of making 100 wagers and winning 35 or less of them. At 35 wins and 65 losses, the number of units lost is 36.5. A unit is the amount of bets won minus 1.1 times the amount of bets lost (1.1 because we must pay an extra 10% on our losses at 11 to 10 odds). So, if you want basically no chance of losing your entire bankroll and have a 50% chance of winning each game with 100 expected wagers to be made then you should divide your bankroll by 36.5 and make that your standard bet. So, if your bankroll is $5000, then your standard bet using this example would be $137. That number represents the amount you are trying to win on each wager and could be rounded up to $140 or down to $130 if necessary. Below I will list the percentage of your bankroll to be wagered on using varying expected win percentages (P) at varying number of wagers (N) expected to be made.

P = .50 | P = .525 | P = .55 | P = .575 | P = .60 | |

N = 25: | 5.9% | 6.4% | 6.9% | 7.7% | 8.5% |

N = 50: | 4.0% | 4.5% | 5.1% | 6.0% | 7.2% |

N = 75: | 3.2% | 3.7% | 4.3% | 5.3% | 6.8% |

N = 100: | 2.7% | 3.2% | 3.8% | 4.9% | 6.7% |

N = 125: | 2.4% | 2.9% | 3.6% | 4.7% | 6.7% |

N = 150: | 2.1% | 2.6% | 3.2% | 4.5% | 6.7% |

N = 200: | 1.8% | 2.3% | 3.0% | 4.4% | 6.7% |

N = 250: | 1.6% | 2.0% | 2.8% | 4.4% | 6.7% |

N = 300: | 1.4% | 1.8% | 2.6% | 4.4% | 6.7% |

N = 400: | 1.2% | 1.6% | 2.4% | 4.4% | 6.7% |

N = 500: | 1.0% | 1.4% | 2.3% | 4.4% | 6.7% |

Remember, it is best to be conservative in estimating the percent of games that you expect to win.

**3) Wager a Constant Unit Amount on Each Play Throughout the Season:
**Many money management systems that I've seen suggest varying your standard wager based on a fixed percentage of the current size of your bankroll each week. Thus, if you started the season with a bankroll of $10,000 and bet 5% of your bankroll on each game, you'd be starting the season betting $500 per game. If you won $1000 that first week, you'd then be betting 5% of $11,000, or $550, on each game the next week. If you had lost $1000 then you'd be wagering 5% of $9,000, or $450. And as the bankroll rises and falls, so would your betting unit for that week. The problem with this system is two-fold. Let's say that you are in a 10 week season and you bet 10 games per week. You start the season with a $10,000 bankroll and bet 5% of your current bankroll on each game for that week. Below is a week by week breakdown of that 10 week season in which that person won 56% of their bets.

Week | Bankroll | x 0.05 = | $ Per Game | Wins-Losses | Profit/Loss |

Week 1 | $10,000 | x 0.05 = | $500 | 8-2 | +$2,900 |

Week 2 | $12,900 | x 0.05 = | $650 | 7-3 | +$2,405 |

Week 3 | $15,305 | x 0.05 = | $770 | 4-6 | -$2,002 |

Week 4 | $13,303 | x 0.05 = | $670 | 6-4 | +$1,072 |

Week 5 | $14,375 | x 0.05 = | $720 | 7-3 | +$2,664 |

Week 6 | $17,039 | x 0.05 = | $850 | 5-5 | - $425 |

Week 7 | $16,614 | x 0.05 = | $830 | 6-4 | +$1,328 |

Week 8 | $17,942 | x 0.05 = | $900 | 5-5 | -$450 |

Week 9 | $17,492 | x 0.05 = | $870 | 6-4 | +$1,392 |

Week 10 | $18,884 | x 0.05 = | $940 | 2-8 | -$6,392 |

Total | $12,492 | 56-44 |

This person profited $2,492 on a 56-44 season, but had he bet $500 per game all season long, he would have been up $3,800 ($500 x 56 - $550 x 44). The problem was that the best part of his season was the first two weeks, when he was wagering the smallest amounts per game. Upon building a substantial profit, his worst week (the final week) came when he was betting the most. There is no reason why games in the middle or end of a profitable season should be worth a larger wager than bets made early in the season when the chance of winning each wager is the same. How about a handicapper that starts slow and ends strong. Let's flip-flop the order of the weekly results of the previous bettor and see what would happen.

Week | Bankroll | x 0.05 = | $ Per Game | Wins-Losses | Profit/Loss |

Week 1 | $10,000 | x 0.05 = | $500 | 2-8 | -$3,400 |

Week 2 | $6,600 | x 0.05 = | $330 | 6-4 | +$528 |

Week 3 | $7,128 | x 0.05 = | $360 | 5-5 | -$180 |

Week 4 | $6,948 | x 0.05 = | $350 | 6-4 | +$560 |

Week 5 | $7,508 | x 0.05 = | $380 | 5-5 | -$190 |

Week 6 | $7,318 | x 0.05 = | $370 | 7-3 | +$1,369 |

Week 7 | $8,687 | x 0.05 = | $430 | 6-4 | +$688 |

Week 8 | $9,375 | x 0.05 = | $470 | 4-6 | -$1,222 |

Week 9 | $8,153 | x 0.05 = | $410 | 7-3 | +$1,517 |

Week 10 | $9,670 | x 0.05 = | $480 | 8-2 | +$2,784 |

Total | $12,454 | 56-44 |

This person profited $2,454 on his 56-44 season, once again short of the $3,800 profit that would have been achieved had he bet $500 per game on every game for the season. The problem here was that the slow start lowered the amount bet per game and when this bettor had good results at the end of the season he was wagering less per game than when he started the season 2-8. Let me give an example of a sports bettor with the same distribution of weekly results in a more consistent pattern (28-22 in the first 5 weeks and 28-22 in the last 5 weeks).

Week | Bankroll | x 0.05 = | $ Per Game | Wins-Losses | Profit/Loss |

Week 1 | $10,000 | x 0.05 = | $500 | 5-5 | -$250 |

Week 2 | $9,750 | x 0.05 = | $490 | 6-4 | +$784 |

Week 3 | $10,534 | x 0.05 = | $530 | 7-3 | +$1,961 |

Week 4 | $12,495 | x 0.05 = | $620 | 4-6 | -$1,612 |

Week 5 | $10,883 | x 0.05 = | $540 | 6-4 | +$864 |

Week 6 | $11,747 | x 0.05 = | $590 | 5-5 | -$295 |

Week 7 | $11,452 | x 0.05 = | $570 | 7-3 | +$2,109 |

Week 8 | $13,561 | x 0.05 = | $680 | 2-8 | -$4,624 |

Week 9 | $8,937 | x 0.05 = | $450 | 8-2 | +$2,610 |

Week 10 | $11,547 | x 0.05 = | $580 | 6-4 | +$928 |

Total | $12,475 | 56-44 |

Even in a pretty consistent season, this money management strategy once again fell short of the $3,800 profit that would have been won by simply betting the same amount on each game. Basically, a money management strategy that adjusts the standard wager based on a fixed percentage of the current size of your bankroll each week will only achieve better results in the absence of losing weeks. Since every handicapper has losing weeks, this strategy will rarely, if ever, outperform a system of betting a fixed standard amount per game for the entire season.

It is okay to vary the size of your wagers based on the strength of the play. For instance, my Best Bets are rated 2-Stars, 3-Stars, and 4-Stars (I also have a very rare 5-Star rating). I make a 3-Star Best Bet my standard unit with 2-Stars being two-thirds of that amount and 4-Stars being 1 1/3 the amount of a 3-Star. Once the amounts to be played on a 3-Star are set, I set the amounts to be played throughout the season on my 2-Stars and 4-Stars based on the amount to be wagered on a 3-Star. The size of each 2-Star, 3-Star and 4-Star wager remain constant throughout the season.

**4) Incorporating a Progressive Bankroll With Constant Unit Wagers:**

In the section above, I showed you that betting a constant standard amount per game will make you more profit than varying the amount played per game each week based on your current bankroll. However, there is a money management method that I've used that can combine progressive per game betting amounts while utilizing a constant betting unit. This particular method should only be used by handicappers that have a proven, winning track record. The reason that varying the size of your standard wager on a weekly basis fails to maximize profit is because losing weeks can not be avoided during the course of the season and you will always lose more per game in the losing weeks than you will win per game in your winning weeks. However, while even handicappers that win in the long run have losing weeks, it is less likely that they will have a losing record for a longer span of time. This is the case because as the amount of games played goes up, the percentage of winners is more likely to approach the true long-run percentage of the handicapper. If a true 60% handicapper picks 5 games per week to bet on, he has a 32% chance of having a losing record and losing money. However, if he bets 5 games per week for 6 weeks he has an 18% chance of losing money during that span of 30 games.

**The idea of this money management system is to break the season down into sections that will give you a high likelihood of having a winning record for that span of time.**Set the standard amount to wager on each game based on your beginning bankroll

**and adjust that amount wagered per game after each section is completed.**However,

**never lower the standard wager even if you've lost in the previous section.**By doing this, you will not be making back less per game in a winning section of the season than you lost per game in a losing section.

If you study your past results and realize that you rarely have a losing record over a 5 week span, then break down the season into 5 week increments. If it takes 9 weeks to assure a good chance that you will have a winning record over that amount of time, then break the football season into two 9 week sections. Personally, I break down the football season into four 5-week sections. Let's say I've got a bankroll of $10,000 to start the football season and I expect a winning percentage of 57.5% on 200 wagers. From the chart I outlined in part 2 of this section I will use a constant 4.4% of my bankroll as my standard wager (my standard play is a 3-Star Best Bet). I will play 2/3 of that amount, or 2.9% of my bankroll on my 2-Stars and 5.9% on my 4-Star Best Bets (1 1/3 the amount of a 3-Star). To simplify things in this example, I will make every play a standard 3-Star Best Bet. So, I start the season with a $10,000 bankroll and after the first 5 weeks, betting to win $440 on each game, I have made a $2,684 profit on a record of 27-19. My bankroll is now $12,684 as I start the second of my 4 sections of the season. I can now step up the size of my wagers to reflect the increase in my bankroll after the first section of the season. In the second 5 week section of the season I will wager to win $560 on each game. In the 5 weeks of my second section of the season I compile a record of 35-23 and make a profit of $5,432. My bankroll is now up to $18,116 as I enter my 3rd section of the season. I am now wagering $800 per game (4.4% of $18,116). Unfortunately, I have slumped a bit and I produced a Best Bet record of only 26-30 in the 3rd five-week section of the season for a loss of $5,600. My bankroll is now down to $12,516, but I will retain my standard wager of $800 per game since my rule is to never lower the standard wager even if I suffered a loss in the previous section of games. In the final 5 week section, I have come back to go 27-13 for a profit of $10,160 and a final bankroll of $22,676 on a record of 115-85 (57.5%). If the sequence of my records each section had been different, I still would have made more of a profit than had I simply wagered $440 per game for the whole season, which would have resulted in final bankroll of $19,460. The worst result using this method would have been to start the season with my best section (27-13) followed by the 35-23 section, the 27-19 section and then ending with the section that resulted in a losing record of 26-30. But even then the final bankroll would have been $19,579, which is still a bit better than having played the same amount on each game throughout the season. Remember, this method should only be used by handicappers with a proven long-run track record of actual winning seasons wagering on sporting events. I suggest at least 56% winners wagered on over at least a 5 year period. If those qualifications do not fit you, then you should wager a constant standard amount per game as outlined in parts 2 and 3 of this section on money management.

5) Reviewing the Steps to Better Money Management:

5) Reviewing the Steps to Better Money Management:

**Step 1)**Establish a Bankroll that represents an amount of money that you can comfortably afford to lose over the course of a season that would still enable you to live the lifestyle you wish to live without stressing too much about the loss of your bankroll should it happen.

**Step 2)**Set your expectations on how many games you expect to wager on during the course of the upcoming season and on a realistic proven percentage of winners that you can expect. If you have an short or spotty track-record, then I suggest using 50% as your expected winning percentage. Next, use the chart in part 2 above to find the constant standard amount per game to be wagered.

**Step 3)**Wager the same standard amount per game for the course of the season, regardless of how well or poorly you are doing up to that point in the season. See examples in part 3 above on how varying your wagers on a week-to-week basis will always result in less profit during a winning season. If you have a proven track record of success (at least 56% over at least 5 years), you should then consider the money management system outlined in part 4 above.

**Step 4)**If you don't have a track-record of winning in the long-run then no money management system will keep you from losing in the long-run. If that is the case, then you should consider purchasing my Best Bets each week online or calling me at (800) 844-9695 for details on signing up at a discounted rate for my 800# Best Bets line. Read about my Best Bets each week in the NFL and College Football main pages on this web-site.

**Handicapping Theory:
**There are many theories on how to handicap a sporting event, but in this section I will focus mostly on the main 3 methods of handicapping. Those 3 methods of handicapping are value handicapping, fundamental analysis, and technical analysis. Below, I'll explain each of these methods and then incorporate all 3 into what I will refer to as rational analysis.

**Value Handicapping:**

This is the oldest method of handicapping a sporting event and the basic premise is bet those games in which you believe the oddsmaker has made a mistake in setting the pointspread. Value handicappers try to make an accurate pointspread on each game and find the games in which their line differs significantly from the actual pointspread that he can play against. For example, if a value handicapper studies a game and determines that Team A is 6 points better than Team B under the circumstances pertaining to their upcoming game and the actual pointspread on the game is 3 points, then he would consider Team A to have 3 points of value in their favor and would then consider making a wager on Team A minus the 3 points. If the actual pointspread on this contest had Team A favored by 8.5 points, then there would be 2.5 points of value favoring the underdog Team B and the handicapper would then consider making a wager on Team B plus the 8.5 points. Had Team A been favored by 6 points, then there would be no value favoring either side. To be a good handicapper using this method you need to first find a good way to determine what the true line should be on a game and once you can do that you need to know how much value (or differential from the pointspread) is needed to produce the results that you want. The second part of that puzzle is easy to solve and later in this section I will give you the likely chance of winning a bet given the amount of value that favors your side. The tough task is to find a method that will allow you to make a better line than the oddsmakers. Since we know that the oddsmakers are influenced heavily by public perception, it is certainly reasonable to expect that a true line can be found that will differ significantly from the actual pointspread.

**Power Ratings:**Most value handicappers have a set of ratings, most often referred to as power ratings, that gauge the overall strength of each team where the difference in ratings is the predicted point differential between the teams if they met on a neutral field. Of course, teams don't usually meet on a neutral field so points are added to the home team to compensate for the advantage that most teams have playing at home. The home field advantage can be a set amount for all teams (such as 2.5 or 3 points in the NFL) or can vary from team to team depending on their individual variance in their level of play at home and on the road. While the concept of power ratings is rather simple, it is very difficult to come up with a set of accurate ratings. The problem with most power ratings methods is that the ratings are generated using some sort of mathematical process based on the past performance of each team and the level of opposition that they have faced. An example of this is the Sagarin Ratings seen in USA Today each week. I've talked to many amateur handicappers that use the Sagarin Ratings to figure out if the pointspread is too high or low on a particular game. What is important to remember is that the Sagarin Ratings, and any other mathematically produced set of ratings, explain what has happened rather than what will happen. In other words, while it is true that these ratings accurately reflect the difference in the performance of each team up to that point of the season they are not a predictive tool to be used to forecast the future performance level of teams, which is what we are truly interested in as handicappers. If beating the pointspread were as easy as picking up the Tuesday USA Today, checking the Sagarin Ratings and making your wagers based on that, then everyone would be winning and sports books would all be out of business. Obviously, that is not the case. So, while the Sagarin Ratings can be used to see how teams have performed up to that point of the season, do not depend on them to forecast how teams will perform in their next game.

**Math Model:**

### Most handicappers that try to come up with a formula to predict future games tend to make the same mistake. That mistake is using regression analysis to find the correlation between different statistics and point differential. While that exercise is very useful for explaining which statistics impact a game's result, regression is not necessarily useful in using past statistical averages to predict future results since some important statistics simply don't correlate very highly to the future. For example, turnovers are the number one factor in point differential in football, but turnovers are also the least predictable statistic. A model that is based on regression analysis will weigh turnovers very highly, but since past turnovers do not correlate highly with future turnovers such models will over-weigh the affect of past turnovers – creating a model that is good at explaining what has happened but not very good at predicting what will happen.

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My math model incorporates the predictability of past statistics to future games and uses each team's compensated statistics rather than their raw stats, which adds to the accuracy of my prediction. Compensated statistics are derived by comparing a team's statistics to the statistics of the opponents that they have faced. For instance, a team averaging only 3.6 yards per rush on offense is actually a better than average running team if they have faced a schedule of opponents that combine to allow an average of just 3.4 ypr on defense. Using compensated statistics in combination with the predictive nature of each statistic used in my model produces an accurate measure of the true differences between two teams future performances – not the difference between their past performances. I also adjust my projected numbers based on current personnel for each team and those extra hours of statistical work have paid off handsomely over the years (and I get better each year at making those adjustments). **I also take out meaningless plays such as kneel downs at the end of a half or game and quarterback spikes, so the game statistics that I use are more representative of a team's performance than the statistics used by other handicappers**.

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I've been using my current math NFL model for 8 years and the record is a very good **199-140-8 ATS (59%)** when my math model prediction is 5 points or more away from the actual pointspread and my College math model had produced 57% winners from 2001 to 2004 using differences of 7 points or higher. I made improvements to my College Math Model prior to the 2005 season and the results have been even better than I anticipated. My College math model kicks in week 5 and it has been 55% picking every single College game from week 5 on since 2005 and an incredible **60%** ATS in games where the difference between my prediction and the line is 6 points or more (5.8 or more, actually) as long as both teams had played 3 or more games.

**How much value is enough?**

If you do have a set of power ratings or a math model that you believe will beat the oddsmaker then your next step is to determine how much value, or how many points difference from the line, you need to have to make a play on the side that your ratings favor. Each point of real value in football is worth about 3% in your chance of winning your bet, but a difference of 3 points from your power rating/math model line from the actual pointspread does not mean you have a 59% chance of winning that bet unless the difference between your line and the actual line is 100% the mistake of the oddsmakers (i.e. the actual true line is your line), which is certainly not the case. Unless you have tested your ratings or model over time, you can not assume that you make a better line than the oddsmakers - and you probably don't unless you've spent years working on a model that has proven to beat the pointspread going forward. Power ratings and math models will only work for you if the prediction you have on a game is more accurate than the actual pointspread that you are betting into, so it's important to test how well your ratings or model predict future games.

I suggest creating a spread sheet of your power rating or math model predictions that contain the actual line (in terms of the home team, where favorites are negative numbers. Thus, a 7 point home favorite would be -7), your power rating/math model prediction (also in terms of the home team, but use positive numbers if you favor the home team and negative numbers if your ratings forecast the home team to lose), the line differential between your line and the actual line (your line + actual line, so positive numbers represent a play on the home team and negative numbers represents your model picking the road team), actual game point differential (home score - road score) and home pointspread result (1 if the home team covered the spread, 0.5 for a push, and 0 for a spread loss by the home team). After compiling a year or two of actual predictions and results - not back-fitted predictions using games that you used to derive your model - you can begin to see if your model is better than the actual line. Simply use statistical software, or analysis available on Excel, to create a regression equation predicting home team spread result as a function of the line differential of your power ratings/math model from the actual line. For instance, I have 6 years using my NFL math model and the equation to predict the chance that the home team covers the spread is .505 + 0.0128xLD, where LD is the line differential between my math model prediction and the line. So, for every point differential, I can add 1.28% to my chance of winning (which is about 50% of the actual value of a points of true line value - so the difference between my model and the actual line is about 50% the mistake of the oddsmakers). If my model projects a 4 points home favorite to win by 10.0 points, then they would have a 58.2% chance of covering based on the past predictability of my math model (.505 + 0.0128x6.0 = .5818), without accounting for any positive or negative situations applying to that game (situational analysis is explained later).

Remember, it doesn't matter how much of a differential there is between your ratings/math model if your line is not proven to be better than the actual pointspread.

**When the majority of wagers on a game are placed on one side, the sportsbooks will move the pointspread in favor of the team that is being more heavily bet on. By doing so, the book is hoping to entice people to bet the other way to even out the amount of money wagered on each side. Some sports betters look for significant moves in the pointspread and go the opposite way of the move. The thinking of those that play against the line moves is that the oddsmaker is more likely to be correct with the line he set at the beginning of the week than the public is in moving it based on their betting. Let's say the pointspread on a game is Team A favored by 3 points over Team B. The large majority of the money bet is coming in on the side of Team A minus the points. In an effort to balance the books, the sportsbook moves the pointspread up to 3 _ points. However, the money still comes in on the side of Team A and after a series of line moves, the pointspread on game day has moved all the way up to Team A by 6 points. A value handicapper might look at a wager on the underdog, Team B, based on what is perceived to be 3 points or value (the amount the line moved). There are also those people that bet in favor of line moves figuring it is a hot bet for some reason or another. There have been many studies done on line movements and they tend to conclude that there is no real advantage in going with a line move (after it has moved) or against a line move. My advice to is handicap a game on its merits and not because the line moved.**

Line Moves:

Line Moves:

**Fundamental Analysis:**Fundamental analysis is the old fashioned way of handicapping. Fundamentalists look at matchups or try to envision how a game will play out. They study the strengths and the weaknesses of teams and try to determine if one team has a significant advantage over another based on their ability to exploit a team's weakness with their strength. For instance, Denver is a great running team and they should have no problem running against a team like Cincinnati that has trouble stopping the run. Or, Carolina's All-Pro defensive end will take advantage of their opponents rookie left tackle and be in the quarterback's face all day. Such a style of handicapping depends on a very keen knowledge of each team and their personnel. The problem with this sort of fundamental analysis is that most mis-matches in a game are already reflected in the pointspread. This is especially true in the NFL, where a high level of talent is abundant at almost every position on every team. Differences between good teams and bad teams are most often a reflection of preparation, desire and experience. However, in college football fundamental analysis can be very useful as there are times when one team simply can't stop the attack of another due to big disparities in size or speed. Or, a defense can be so much more talented than the opposing team's offense, giving the opponent very limited chances to score. For instance, If USC's offensive line has an average size of 6'5" and 305 pounds and is going up against a team with a defensive front averaging 6'3" and 230 pounds, then the Trojans will likely score on every series by simply running the football on nearly every play. Of course, in this scenario you would also most likely be laying a huge amount of points as that mis-match would be reflected in the line. The key to fundamental analysis is finding statistical indicators that have led to pointspread success. In fact, there are some very profitable fundamental indicators in college football that I use that are based on certain statistical information.

**Which statistics are good indicators of pointspread success?**It is good to understand which statistics lead to pointspread success, but the numbers that generally reflect the pointspread winner in a contest may be useless in trying to predict future pointspread winners. For example, turnover margin has the highest correlation between winning and losing in the NFL and even a higher correlation to the pointspread winner in a game. In fact, the team that turns the ball over the most in an NFL game will cover the pointspread only 20 to 25 percent of the time. The problem with that information is that turnovers are also the most unpredictable facet of football. In fact, taking teams that have the worse turnover ratio entering a game is a better strategy for beating the pointspread than taking the team that has been positive in turnovers. My point is that there is a difference between a statistical relationship and a statistical indicator. There is a huge relationship between turnovers and winning percentage but turnovers can be a deceiving indicator of future pointspread results. The more predictable a statistic is the more likely it will be a better indicator of future events. Aside from turnovers the next strongest relationship to pointspread success is rushing the football. In football, the game is usually won at the line of scrimmage and teams that control the point of attack are more likely to win and cover the pointspread. Rushing statistics are a reflection of which team controlled the line of scrimmage and betting on teams that can control the line of scrimmage is usually a good thing. However, while there is a statistical correlation between rushing yards and covering the pointspread, it should be pointed out that this relationship could be largely due to causation. Often times, rushing yards are the result of a big lead rather than the cause of it, as teams with huge leads tend to run the ball more in the latter stages of a game to eat up the clock and minimize mistakes while the trailing team has to throw the football in an effort to catch up while eating less time off the clock. Perhaps running the football effectively had nothing to do with a team building a huge lead, and the disparity in rushing yards occurred as a result of that big lead. Then again, often times teams control the game and do cover the pointspread because of their superior rushing attack and, in general, teams that average more rushing yards are better teams and those teams are more likely to win and cover the pointspread. So, I do believe that the correlation between rushing yardage and pointspread success is a valid handicapping tool, but we must be careful not to put too much weight on relationships between game statistics and that game's result. What is important for us is to find relationships between past statistics and future games. We need to find statistics that can help us predict the future rather than statistics that help explain the past. As I said above, turnovers are not a positive indicator of pointspread success because there is very little correlation between past turnovers and future turnovers. However, there is a relationship between season-to-date rushing statistics and future pointspread results. My readers will benefit from the research that I've done in finding the relationship between season-to-date rushing stats and pointspread success. While there is a forward looking relationship between rushing stats and pointspread success, the same cannot be said of passing yards. Teams that throw for a large amount of yards generally do so for two reasons. They don't have a consistent running game and need to throw to move the football, or they are behind and need to throw to try and catch up. Either way, a large amount of passing yards is usually a reflection of a team on the short end of the final score. That's not to say that you don't want a good passing game, but passing should not be measured by yards thrown for, but rather by yards per pass. However, even yards per pass has proven to be an unreliable indicator for future pointspread success. Part of the reason for that is that throwing the football produces more variable results than rushing it and thus is less predictable from game to game. Also, quarterback is such a high-profile position and the ability of a team to throw the football effectively is usually already incorporated into the pointspread, creating no value.

There are fundamental indicators that I have used successfully for many years and I have created a set of statistical profiles that have proven to be very useful in projecting how teams with certain statistical characteristics perform against the pointspread when facing a team with other statistical characteristics. Those fundamental indicators and statistical profiling are a part of my selection process along with the technical factors that I discuss below.

**Technical Analysis:**

Technical analysis is the study of patterns and is based on the psychological ups and downs of teams as well as the psychological patterns of those that bet sports. Obviously, teams have their ups and downs and I use trends and situations to identify when teams are likely to play well and when they are not based on patterns that have lead to pointspread success and failure in the past.

**Team Trends:**The most widely used and understood type of technical analysis is the study of team specific patterns which I simply call team trends. When I started handicapping back in the mid-80's team trends were an important part of selecting which teams I bet on. I would go back into my logs of results and study how teams performed at home and on the road, as a favorite or as an underdog, after a win or after a loss, and under other circumstances. I found that the most insightful team trends were the ones that were based on recent performances and explained how teams performed after good and bad performances. For instance, the 49ers were 48-17 ATS (Against The Spread) from 1981 through 1997 when they lost straight up and failed to cover the pointspread in their previous game. What this told me is that the 49ers had a strong tendency to be more focused after a poor outing. This is a trend that worked for many years despite coaching and player changes over the years. Of course, the 49ers have basically had the same type of team over all of those years and the 49ers' tradition started under Bill Walsh in the early 80's has been handed down through the generations of players. It also helps that San Francisco has had only two quarterbacks during those 17 years and that both Joe Montana and Steve Young were very competitive and hated the idea of losing.

Most of the personality of a team comes from the head coach and I have noticed that patterns follow coaches from team to team. For instance, Jon Gruden's teams have a tendency to play much better after a loss (16-8 ATS from 1998 through 2001 in Oakland and 15-10-2 ATS from 2002 through today with Tampa Bay) than they have after a victory (16-20 ATS with Oakland and 13-20 ATS with Tampa Bay). Gruden is very emotional and his teams tend to ride the wave of their coach. Gruden is very good at getting his team fired up and he uses bad performances as motivation for his team the next week. On the flip side, Gruden simply has less ammunition to get himself and his team fired up after a good performance.

There are some types of team trends that simply do not predict what will happen in the future. Over the past 6 years, I have studied the results of all statistically significant team trends that I have used in my game notes and tallied to results, broken down by the type of trend it is. The type of team trends that were the best indicators are what I call personality trends, which are the trends that explain how teams react to recent performances, such as how a team performs after a win or a loss, or after two straight spread wins, or after allowing 28 points or more (like the ones I used in the examples above). Certain types of team trends don't work at all, such as series history trends or trends that deal with a specific game number. A series history trend is a trend that states that Team A has covered 10 straight times against Team B. I have found that regardless of how many times in a row a team has covered against another team, the chance that they cover in the next meeting is 50%. A trend that says that Team A has gone 13-1 in their second road game of the season or is 8-0 in week number 5 doesn't make any sense and does not have any value in predicting the future. I know what types of trends tend to work and to what degree they work.

While the use of team trends worked very well during the 80's and through the mid-90's, the advent of free agency and the constant changing of head coaches in the league changed the personalities of teams every few years, making previous patterns of these teams meaningless in most cases. I tend to shy away from most team oriented trends unless the head coach or core of star players has been intact over the term of the trend. I certainly wouldn't pay much attention to a Carolina Panthers trend that included games prior to the arrival of head coach John Fox - who changed the personality of that team. On the other hand, longer term trends of the Philadelphia Eagles do have some validity due to the long tenure of head coach Andy Reid - even though the personnel have changed over the years.

Team trends can still be a very effective handicapping tool, but don't use team trends that no longer explain the personality of a team. From a statisticians' point of view, a trend is basically a sample of games taken from a pool of results. When the pool from which the sample was taken changes, the sample of games is no longer representative of that pool and should thus not be used as a forecasting tool. Thus, team trends work best with teams that have had the same coach or core group of players for at least as far as the trend goes back.

**Situational Trends:**As the use of team trends became more limited because of free agency and coaching changes, I began looking for patterns that explained the results of all teams that were in the same set of circumstances. For instance, how did all teams perform following consecutive games in which they allowed less than 10 points? Or, what is the record of Monday night home underdogs? These league-wide patterns are referred to as situational trends. I have found that situational trends are better indicators of future pointspread results than team trends are because team specific changes (such as coaching changes and free agency) have little effect on league wide patterns. The patterns that exist in the NFL and in college football have existed for years and are based on the psychological ups and downs that exist in all teams and in the wagering habits of the betting public. While all of my situations deal with the patterns that exist in team performance, some of them also are enhanced by the betting patterns of the public, who are influenced by more recent performances of a team. A lot of the situations that I use deal with playing on teams that have been playing below expectations (bounce-back situations) and playing against those that are playing well in recent weeks (letdown situations). Bounce-back and letdown situations work partly because the betting public overreacts to a string of good and bad performances and bets accordingly. A team may become out of favor after a couple of terrible performances while other teams may get more support than warranted from a couple of very good performances. Since the pointspread is a measure of public perception rather than reality, the pointspread gets over-adjusted in conjunction with the public's fear of betting on a team on the slide or with their eagerness to bet on a team playing especially well in recent weeks. This sort of betting behavior based on short-term results gives the smart player line value and that is why these sort of bounce-back or letdown situations produce good results. For instance, NFL teams that win back-to-back games straight up as an underdog are just 40-68-2 ATS (Against The Spread) in their next game if they are on the road and not getting more than 7 points (since 1981), including 19-48-1 ATS if visiting a non-divisional opponent. There are a couple of reasons for this. First, teams that win back-to-back games as an underdog tend to get more support from the betting public and as a result the pointspread deviates from a realistic pointspread to a line that represents current public perception of the “hot" team. At the same time, the team that has just won back-to-back upsets generally is not quite as hungry, as the coach has less ammunition to motivate them with. Also, the coaching staff is generally reluctant to change the offensive and defensive schemes that produced the two victories. Their next opponent, however, has two weeks of films to figure out how to find a weakness in the schemes that have been so successful and these opposing players and coaches will prepare for the game against the “hot" team with more focus because of their recent success. So, not only is the “hot" team in this situation due to play at a lower level but the pointspread has also moved in our favor to create value because the betting public is generally afraid to bet against a “hot" team. Playing against a non-divisional opponent gives the team off two upset wins even less reason to get fired up. This situation does not work nearly as well when playing against home teams off back-to-back upset wins because it is easier to maintain a high level of intensity in front of the home fans.

Not all situations are in the bounce-back or letdown mode and take advantage of misguided public perception and the natural fluctuations of team performance. There are also what I call momentum situations and these deal with playing on teams that are playing well and playing against teams that are playing poorly. For instance, in the NFL home underdogs are 173-128-9 ATS if they won straight up as an underdog the previous week. Bad teams in the NFL generally lack the confidence to beat a good team and there is nothing like an upset win to boost confidence. The confidence of winning as an underdog is enhanced by playing in front of the home fans and thus creates a good momentum situation. In general, the NFL is a contrary league, meaning that most of the situations involve going against teams that have been playing well and going with teams that have been struggling. College football, meanwhile, is more of a momentum sport and much more of the good technical situations in college football involve playing on a team that has been playing above expectations.

**Does Technical Analysis Work?**Technical analysis has come under scrutiny by fundamental handicappers and some sports bettors due to the fact that anybody searching a database randomly for patterns will find situations that have produced very good results. However, the key is to look for situations that make sense. I don't use trends such as "so and so is 13-2 in week number 7" (does a team actually know that week 7 is their week and gain confidence from it?) or “bet on home dogs from +2 to +4 if such and such" (the more narrow the pointspread range is the more likely it is a random occurrence and not a true indicator of a real pattern). So how can I be sure that technical analysis works? I have done research on the predictability of both team trends and situational angles and both have proven to work to forecast the future, although most team trends no longer have the sample size to be significant given that I only use team trends that are compiled under the current coach. The record of situational trends that apply to all teams is much better as far as forecasting the future. At the beginning of each year, I make a list of the situational angles that I think are meaningful (they are all easily statistically significant). At the end of the year, I tally the results of these angles. In the last 8 years of doing this, I have found that the situational angles that I use (remember, if you're angles don't make sense they are not going to hold up as well) have won at a profitable rate of 56%, but the situations with a higher statistical significance (i.e. a higher t-value) have proven to be even more predictive.

Many handicappers tend to back-fit past data by adding more and more factors (parameters) to a situation until they have a very high percentage angle (but also a much smaller sample size). However, my research has shown that a situation's predictability is sacrificed with each parameter added to derive that situation. For instance, a situation with a record of 50-20 (71%) that is derived using 10 factors isn't as predictive as the 57.4% home underdog situation that I presented above, which has just 4 parameters (this game home, this game dog, won last game, dog last game) and a much larger sample size. It's easy to find a very high-percentage situation if you use an unlimited number of parameters to get to that situation, but all that will result in is a situation that explains what has happened rather than something that helps predict what will happen. My research, and the theories of statistics, shows that the more predictive angles have fewer factors and a larger sample size, rather than a smaller sample situation with a high winning percentage that was derived by using too many parameters. Further research I did in the Summer of 2004 enabled me to accurately assess a situation's future performance based on the win percentage, sample size, number of parameters and more recent performance (i.e. record of the angle over the past 3 seasons). That research led to a more realistic use of situational analysis than I've employed in the past. For instance,

**I can now tell you that a situation with a record of 140-60-5 ATS that uses 6 parameters has a 56.8% chance of winning the next time it applies if the line is fair.**Having a realistic expectation of a situation's value has helped my overall analysis immensely the last 2 seasons and I will continue to devote time each summer to update the research on the predictability of my situational analysis.

Remember, just because a situation is 70% over 200 games in the past does not mean that it will win 70% of the time in the future. A 140-60 situational trend is simply a sample of 200 games selected from a population consisting of all NFL games. Since the NFL is constantly changing (although the league as a whole doesn't change nearly as quickly as most individual teams do), the results of the same situation in the future will not fully reflect the past. Also, by definition, a statistically significant trend has a 5% chance of being caused by no more than chance variation, and the record of those trends can be expected to be 50% as a whole, bringing down the overall percentage of all significant trends. There is also going to be a certain level of back-fitting involved in finding a situation, which also lowers the future percentage of the situation. Of course, the better the record, the greater number of games in the sample, and the fewer parameters there are in an angle the more likely that the situation is real and not just random.

**Combined Analysis:**

The key to the 2004 research on my methods was finding a way to combine my situational analysis, fundamental indicators and my math model to give me an overall chance of a team covering at any given number. My performance on my Best Bets the last 2 seasons is an indication that I succeeded in that endeavor and I will continue to refine the accuracy of my methods each year. An example of combined analysis is a game in which Team A applies to a 140-60-5 ATS situation that uses 6 parameters. Team B applies to a statistical profile indicator with a record of 86-28-4 ATS and my NFL math model favors Team A by 12.4 points when Team A is a 7 point favorite in reality. As discussed above, a situation with a record of 140-60-5 and 6 parameters has a 56.8% chance of winning if the line is fair. The fundamental indicator favoring Team B has a 58.2% chance of winning given a fair line and my math model would give Team A a 56.9% chance of covering at a line of -7 points. The trick is assigning a point value to the situation and the fundamental indicator based on their chance of covering at a fair line. I simply put everything in terms of points based on the relationship between point differentials and the chance of covering of my math model. Each point difference in my math model is worth about 1.3% in chance of covering, so each percentage point is worth about 0.8 points (1/1.3). In this case, the situation favoring Team A is worth 5.3 points while the fundamental indicator favoring Team B is worth 6.4 points. My math model favors Team A by 10.4 points, so adding the value of the situation and the indicator would result in an overall prediction of Team A by 9.3 points (+5.3 - 6.4 + 12.4 = 11.3), which would give Team A a 55.5% chance of covering at the line of -7 points. Obviously, things can become a lot more complicated when there are multiple situations and indicators applying to a particular game - which is most often the case, but my years of studying probability theory have given me the tools to sort through it all and come up with an accurate measure of the overall affect of the situations and indicators.

**Best Bets:**

The last 2 seasons I've used a minimum overall predicted chance of covering of 58% or 59% before making a game a Best Bet and those Best Bets ended up hitting 59.3%. There is variability in all games of chance, but I expect to win 58% or higher on my Football Best Bets in the long run using the methods that I have employed.

**A lot of handicappers use situational analysis and math models in their handicapping but few, if any, of them have studied the predictability of their methods, as I have, or found a realistic way of combining their methods for an overall measure of predicted success on every game.**