ntroduction

From the previous section, it is easy to notice that selecting
who is going to *win* a game match is easier that selecting
who is going to *cover* the point spread. In the same way, it
is easier to select who is going to *lose* the match that select
who is going to *no cover* the bet.

In order to use these results in a consistent way, a running average algorithm is integrated in the formulation. The objective of the algorithm is to smooth the outlier so they compensate each other while still obtaining a continuos outlier shape in the two ends of the plot. This technique is used in the stock market for defining stocks trends.

lgorithm

The process is as follows: the sorted winning percentage position of the teams is taken to obtain the covering percentage of that position.

The algorithm is performed on the two outliers. One is for the *THwpct* teams with *high winning percentages*
starting on the *tHwpct* team with the highest winning average,
moving to the second highest and so on until the running
average fails to overcome the house edge of 2.54%
where *Cpct* is the value of the running average
for the *cover* variable with respect to the position from the
teams with high winning percentage and *tcpct* is the team
covering percentage.

The second instance of the algorithm is
applied to the *TLwpct* teams with *low winning percentages*,
starting on the *tlwpct* team with the lowest winning percentage
and moving to second lowest and so on until the running
average fails to overcome the house edge where
*Npct* is the value of the running average for the *no
cover* variable with respect to the position from the teams
with low winning percentage and *tnpct* is the team no covering
percentage.

** Cover the point spread variable**

The result from applying the running average algorithm to the first subgroup is shown in the plot below, which includes the 1990-1991 to 1994- 1995 NBA seasons (27 teams). In this subgroup, if selecting the six best teams with winning percentages will lead to overcome the house edge of the 2.54%. If selecting the worst seven winning percentages teams will overcome the house edge too.

NBA Seasons 1990-1991 to 1994-1995 (27 teams)

The result from applying the moving average algorithm to the second subgroup, which includes the 1995-1996 to 2003-2004 NBA seasons, with 29 teams is hown in the plot below. The result reveal that the seven best teams with winning percentages will surpass the house edge and the eight teams with the lowest winning percentages also surpass the 2.54% threshold.

NBA Seasons 1995-1996 to 2003-2004 (29 teams)

In the current era of professional basketball with 30 teams, comprising the 2004-2005 to 2013-2014 NBA seasons, the result from applying the moving average algorithm show that the five teams with highest winning percentages and the ten teams with worst winning percentages surpassed the house edge.

NBA Seasons 2004-2005 to 2013-2014 (30 teams)

**Over the total points line (TPL) variable**

Applying the running average algorithm to the over the
TPL variable results in a plot that remains in the house edge
domain. The only notable pattern is a
tendency of the *THwpct* teams with high winning percentages
to have an under ≈1.3% of the times more likelly than an
over. Also a tendency of the *TLwpct* teams with low winning
percentage to have an over ≈1.2% the times more likelly than
an under.

layer Edge

From the regression study in Section egression together with the
proposed algorithm, a couple of correlation emerged between
the variables *W/L↔C/N* and *W/L↔O/U*.

The correlation coefficient assume values in the range from -1 to +1, where +1 indicates the strongest possible agreement and -1 the strongest possible disagreement. This correlation is calculated as the proportion of the extreme outliers from the variables in consideration:

W/L correlation coefficient to C/N is ≈0.2

W/L correlation coefficient to O/U is ≈-0.04

The Player Edge consist in taking groups of teams from the two
outliers, one *THwpct* : Teams with high winning percentages
and the second for the *TLwpct* : Teams with low winning
percentages.

The following table show the *TLwpct* as the twelve low winning
percentages teams to surpass the casino’s edge.

NO COVER the point spread |
Npct Percentage |
Casino Vigorish "Juice" -2.4% for -110 |
Profit |

The most losing team NO COVER the bets |
44.68% |
-2.4% | 2.92% |

The 2 most losing teams COVER the bets |
44.76% |
-2.4% | 2.84% |

The 3 most losing teams COVER the bets |
45.34% |
-2.4% | 2.26% |

The 4 most losing teams COVER the bets |
45.95% |
-2.4% | 1.65% |

The 5 most losing teams COVER the bets |
45.88% |
-2.4% | 1.72% |

The 6 most losing teams NO COVER the bets |
46.19% |
-2.4% | 1.41% |

The 7 most losing teams NO COVER the bets |
46.42% |
-2.4% | 1.18% |

The 8 most losing teams NO COVER the bets |
46.86% |
-2.4% | 0.74% |

The 9 most losing teams NO COVER the bets |
47.22% |
-2.4% | 0.38% |

The 10 most losing teams NO COVER the bets |
47.13% |
-2.4% | 0.47% |

The 11 most losing teams NO COVER the bets |
47.31% |
-2.4% | 0.29% |

The 12 most losing teams NO COVER the bets |
47.54% |
-2.4% | 0.06% |

Less winning teams in NBA seasons 1990-1991 to 2013-2014

In the same
way, the next table shows the *THwpct* as the twelve high winning
percentages teams to surpass the casino’s edge with the
proposed Player Edge. Moreover, the summation of percentages
reveal that it is a better option to bet to the *TLwpct* teams
with low winning percentage to *no cover* the bet with a
culmulative percentage of 15.92% compared to the *THwpct*
with 13.35% to *cover* the bet.

COVER the point spread |
Cpct Percentage |
Casino Vigorish "Juice" -2.4% for -110 |
Profit |

The most winning team COVER the bets |
55.48% |
-2.4% | 3.08% |

The 2 most winning teams COVER the bets |
55.27% |
-2.4% | 2.87% |

The 3 most winning teams COVER the bets |
54.23% |
-2.4% | 1.83% |

The 4 most winning teams COVER the bets |
53.96% |
-2.4% | 1.56% |

The 5 most winning teams COVER the bets |
53.62% |
-2.4% | 1.22% |

The 6 most winning teams COVER the bets |
53.23% |
-2.4% | 0.83% |

The 7 most winning teams COVER the bets |
53.08% |
-2.4% | 0.68% |

The 8 most winning teams COVER the bets |
52.86% |
-2.4% | 0.46% |

The 9 most winning teams COVER the bets |
52.85% |
-2.4% | 0.45% |

The 10 most winning teams COVER the bets |
52.57% |
-2.4% | 0.17% |

The 11 most winning teams COVER the bets |
52.59% |
-2.4% | 0.19% |

The 12 most winning teams COVER the bets |
52.41% |
-2.4% | 0.01% |

Most winning teams in NBA seasons 1990-1991 to 2013-2014

The complete study for the 24 NBA season
(1990-1991 to 2013-2014) is shown in the plot below,
*THwpct* : Teams with high winning percentages as the best
twelve teams with the highest winning percentage to cover the
point spread. and *TLwpct* : Teams with low winning percentages
as the best twelve teams with the lowest winning percentage
to fail to cover the point spread. The groups are defined
untill the running average algorithm falls in the house edge range
of 50±2:54%.

Running average from the teams with worst winning percentage *tLwpct* and for the with the higest winning teams *tHwpct*, showing a covering percentage for the twelve less winning teams that overcomes the house
edge of -2.54%. And for the twelve most winning teams that overcomes the house
edge of +2.54%

est set

From the "training set" results, let's define a threshold of profit > 2% to test the Player Edge strategy. In this sense, the three teams with the lowest winning percentages and the two teams with the highest are considered in the strategy, see previous tables.

In order to test the performance of the proposed Player Edge strategy,
the NBA 2014-2015 and 2015-2016 seasons are used as "test set". The data from applying the Player Edge algorithm, results in profits
for the selected range. The next Figure shows the Player
Edge algorithm until it converges on the two ends. For the *TLwpct* teams
with lowest winning percentages the worst ten team's percentages surpassed
the house edge, while for the *THwpct* teams with higher winning percentages,
only the five most winning team's percentages surpassed the house edge on
the positive end. In the Appendix website a detailed information of the Player Edge algortihm
applied to each NBA season can be found.

Player edge algorithm highligthing team positions that surpassed the house edge for the NBA seasons 2014-2015 and 2015-2016 used as "test set"

onclusion

This study aims to answer the question: *Does the
winning team always covers the bet?* in professional basketball NBA.

A "training set" with the NBA seasons from 1990-1991 to 2013-2014
is used for revealing an indirect factor analysis within the betting variables:
*cover* the point spread to the *team's
winning percentage position*.

A "test set" with the NBA seasons 2014-2015 to 2015-2016 is used for corroborating the hypothetical indirect factor analysis between betting variables.

Also, a Player Edge algorithm and strategy is described, showing a methodology for a possible long-term advantage to the player to surpass the house edge of 2.54%

Moreover, the presented indirect factor analysis can be applied to stock market for defining stocks trends from indirect generalized variables and also for DNA analysis to relate patterns from the indirect generalization of well defined variables.