I can still remember the day when a close friend of mine suggested taking my self-built
neural network simulator I used for identifying trends and ‘sea changes’ in the
currency markets and applying those mathematical methods to the sports and
their respective outcomes.
Although that was a long time
ago, I can also still remember looking at him probably with a deer in the
headlights look, and saying that there was no statistical connection better
than a coin flip to identify sports results and then be able to exploit the
lines on those games. Well, I tinkered around one day and nearly immediately
realized that there were situations where history did help identify
opportunities in the current season for all major sports.
One of the many handicapping
tools I have used for sports that also was applicable in the financial markets
was relative strength indicators. These indicators identified where
current price action was in relationships to past price action. So, if prices
were in a bull trend, the speed of the ascent of the trend would increase the
likelihood of an ‘overbought’ red flag signal being generated. This was a
beginning alert to a possible short-term trend in price action and that a
period of retracement and consolidation would ensue moving forward.
The tracking of relative strength
indices are very useful in spotting unsustainable trends in modern day sports.
If, for example, a poor hitting team, like the Oakland A’s, who are ranked dead
last in 30th position with a .226 team batting average suddenly catches fire
and bats .295 over the most recent five games, an overbought signal would be
generated. This simply means that the current level of batting prowess is not
sustainable nor reflective of the true norm of the team. In such situations, a
retracement, or a series of games where the team bats .250 or worse would
occur. That also implies a lack of significant run scoring and an opportunity
to fade the A’s.
The same can be applied for strong
hitting teams who go into mini slumps. Texas, currently, ranks best in MLB
with a .281 team batting average. There will be a point in the season where
the Rangers will go through a three to five game mini slump and bat perhaps
.225 over that span. When those slumps occur to the top-5 best hitting teams it automatically catches my eye as an ‘oversold’ condition and that those teams
are more likely to bust out of their slumps quickly.
You may be wondering if it is
worthwhile to consider playing on poor hitting teams when they endure slumps.
The answer is a big fat no. When poor hitting teams go into slides, those
slides last far longer than when they are hitting well. So, poor hitting teams
batting slumps will last far longer than their hitting hot streaks. The same
can said of the elite teams in reverse order. They will hit at an elite level
far longer than the slumps.
The use of moving averages in the
charting of financial markets and their respective instruments and securities
is widely popular. They not only identify trend direction, but can also
identify price action that has reached excessive and unsustainable levels.
Again, the same can be applied to sports statistics.
For a homework assignment and
great exercise for you to perform, track your favorite teams team batting
average. On a graph, plot the points for the team batting average in each game,
and then apply a three-game and a 14-game moving average and plot that on the
First, you will have more of a
scattergram type of graph from the game batting averages. However, when you add
the moving averages, they serve to smooth the data and trends will be readily
seen. Also, check what happens to the win-loss records when the 3-day and 14-day
moving averages cross over each other as this is one of many technical tools
used in the short-term trading of financial markets, especially futures,
commodities, and currencies.
So, give it a shot, and I will
show you my chart of the hitting of the Philadelphia Phillies in next