Sun, Dec 4th, 11:02 am | (753) MONMOUTH (NJ) vs. (754) Manhattan | CBK +6 (-110) MONMOUTH (NJ), r=10 | Record | CBK | |

Sun, Dec 4th, 11:02 am | (743) Belmont vs. (744) Illinois State | CBK +137 un -110 Under, r=10 | Record | CBK | |

Sun, Dec 4th, 11:02 am | (306575) Vermont vs. (306576) MERRIMACK (MASS) | CBK +127 un -110 Under, r=10 | Record | CBK | |

Sun, Dec 4th, 12:02 pm | (767) Niagara vs. (768) Quinnipiac | CBK +129.5 ov -110 Over, r=10 | Record | CBK | |

Sun, Dec 4th, 2:02 pm | (779) Minnesota vs. (780) Purdue | CBK +130.5 ov -110 Over, r=10 | Record | CBK | |

Sun, Dec 4th, 3:02 pm | (306585) Florida International vs. (306586) FLORIDA GULF COAST (FGCU) | CBK +151 un -110 Under, r=10 | Record | CBK | |

Sun, Dec 4th, 4:02 pm | (783) Stanford vs. (784) Arizona State | CBK +132.5 un -110 Under, r=10 | Record | CBK |

# Author: admin

I have two plays ready for Saturday’s college football.

Nevada under 53.5-110 that was a client play the line has moved 52.5 is fine.

What are the bankroll implications if we keep the bet size and edge constant but vary the odds? The chart below plots the probability of various drawdowns (from starting bankroll) when a bettor places 1,000 1 unit bets at various odds, with an edge of 4%. Each series of 1,000 wagers was simulated 10,000 times.

Recall that when betting at odds of 2.0, there was a 17.4% chance of being down 20 units at some stage through a series of 1,000 bets. At odds of 5.0, the chance of a 20 unit drawdown increases to just under 60%. With an identical stake, edge and expected return from a series of bets, predominantly backing favourites or longshots has drastically different bankroll implications in terms of variance.

Understanding what type of bettor you are is therefore critical to dealing with the inevitable swings you will experience.

To quantify this variance, consider again a series of 1,000 bets. By varying the odds (implied probability from 10% to 90%) and edge, the chart below plots the standard deviation of returns.

We can see clearly that variance increases as the odds lengthen (or as implied probability decreases), in line with the analysis above. From the chart above, making 1,000 1 unit bets with 10% edge has a standard deviation of 6.5% if all bets are made at 5.0 compared to 2.5% betting at 1.67. In both cases the expected return is +100 units (+10%).

An interesting result is that for odds shorter than 2.0, as edge (and thus expected return) increases, standard deviation actually decreases. Finding an increasing edge in odds shorter than 2.0 is rewarded not only by the increase in expected return but with a reduction in variance.

## BANKROLL MANAGEMENT

For release purposes, I utilize a unit rating system as follows: 1.0, 1.5 and 2.0 with 2.0 being the strongest and the most rare. Majority of the plays will be 1.0 or 1.5 unit plays. For me personally I am quite conservative and a 1.0 unit play represents .5% bankroll, 1.5 unit play represents 1% bankroll and 2.0 unit play represents 1.5% bankroll. I would suggest never exceeding 3% of bankroll on a play. Any play that doesn’t specify a unit of 1.5 or 2.0 should be considered one unit.

While the average result was just under a four unit increase in bankroll, the difference between the best (+38 units) and worst (-30 units) outcomes is substantial. As a bettor it is important to understand variance and be aware that a 4% edge doesn’t guarantee a 4% profit. With this simulation of 100 bets, 90% of the time a bettor can expect a return of between -12 units and +20 units. A 10 unit drawdown (from your starting bankroll) can be expected around 20% of the time, however just 2% of the time a bettor will experience a 20 unit drawdown. Interestingly, 32% of the time a bettor can expect to be down after 100 bets, despite a 4% edge on every bet. If we increase the bettor’s edge to 10% (true probability of 55% for a wager at 2.0), a loss occurred 13% of the time after 100 bets. The chance of a drawdown of 20 units or more was just 0.4%. Of course, as the edge increases, the likelihood of a bad run decreases, but what happens when the number of wagers is increased to say 5,000. The chart below shows the first scenario above (52% true probability, betting at odds of 2.0) simulated 10,000 times.