Weather predictions are based on advanced computer modeling, which use historical and current weather patterns to project potential atmospheric outcomes. A meteorologist will analyze this data and then select the model with the highest probability of occurrence and relay this information to people through media
Weather people and websites are often wrong when predicting the weather and fail to project the precise high and low temperature of the day on a regular basis.
Most people still pay attention to weather predictions because it gives them a good idea of whether or not they need a coat or if they need to remember to grab an umbrella. If someone chooses their attire based on an expected high temperature of 90 degrees and it only reaches 85 degrees, it will not significantly impact their day. Any respectable gangster grabbing his umbrella on his way out the door will tell you it's better to be caught with one than without one.
There are alternatives to looking at your local weather forecast from the newspaper, internet or news channel in order to ascertain what you should expect on any particular day of your life. You can open your front door in the morning and go outside, but the weather is likely to change as the sun faithfully tracks across the sky. If you live in the same area for many years, you begin to develop a pretty good grasp of what the day may bring. If you live in Florida, you can expect a short heavy downpour at least once a day during certain seasons. If you live in Southern California, you know the chance of rain on any one day is rare. The more observations you are able to make over time, the more likely your predictions of the future will be correct.
Examples of this type of informal data crunching to predict weather outcomes can be seen in the The Old Farmer’s Almanac and Farmers’ Almanac, which provide a year-long forecast that is based on prior years’ data and observed weather trends over time. This method for weather prediction is more scientific than simply going outside and looking at the sky, yet meteorologists agree that the two rival publications do not come close to their claim of 80% accuracy over time. Some have even compared the accuracy of these publications to that of Punxsutawney Phil, whom has less than a 50% accuracy rate even though said groundhog is allegedly so ‘close to the dirt’. I assume the people I see walking down our fine KC area urban streets in a winter coat on a 103 degree day in July were faithfully relying on their agricultural almanac of choice which predicted an unseasonably cool week and that it has nothing to do with crack cocaine.
The point of this rambling is that weather is crazy and difficult to predict. Regardless of whether you use advanced statistical computer modeling or the opinion of a rodent, there are plenty of incorrect predictions. Baseball is no different.
There are a wide variety of ways in which to predict or project future success for a baseball player. These range from stepping outside and looking in the sky to advanced statistical modeling using computers.
There is the ‘eye test’; where an individual can go outside (or inside) on any given day to attend a baseball game and observe the performance of a player. If you employed this method and randomly picked a day to observe performance, one may come to the conclusion that Jarrod Dyson is a power hitter or that Joakim Soria is an effective relief pitcher. The more observations that are done by the same eye(s) will provide additional information and at a certain point in time there will be enough observational data to identify outliers in performance and determine approximate level of skill. One can combine that information with descriptive physical characteristics of the player and observed case studies of similar players over time to help them make a informed judgement of what the future performance of any given player is most likely. This is a very basic interpretation of the method used by scouts to project player performance.
There are a variety of methods in which you can use observed and recorded data in a mathematical model in order to predict future performance. There is a plethora of data available for major league and minor league baseball players; this area has made exponential leaps and bounds over the back of a baseball card. Analysis of this data can reveal how closely related these data points are across time on a year-to-year basis for individual players in order to formulate an equation to forecast future performance based on a certain amount of prior years' observed data. These projections range from a simple linear projection system like MARCEL to more advanced systems that use quadratic regression models, regress to the mean and use other methods to more accurately project future performance.
Sometimes, the weather predicts rain that doesn’t come; a high of 83 degrees that ends up being 79. I won’t feel like a Moran for grabbing an umbrella because there was a distinct possibility that it was going to rain. My choice of a t-shirt and shorts doesn’t end up being catastrophic because the high temperature for the day was a few degrees cooler.
Sometimes a scout predicts that a prospect will be a great player and he ends up never making it past AA.
Projection systems will tell you a player will hit 25 home runs and that player will finish with 20, or that a player will have a wOBA of .340 and they end up with a wOBA of .325. Sometimes, that player will get hurt and end up with 4 home runs and a wOBA of .280.
Sometimes Mt. Vesuvius erupts and sometimes you get earthquakes in Kansas City.
I hate weather predictions, they are often incorrect. They are attempting an incredibly difficult task because weather is so unpredictable. I don't quit paying attention to the weather because of this, I take the information with a grain of salt in order to better inform how I can prepare for my day even knowing they aren’t 100 percent precise.
It’s okay for you to hate baseball projection systems, but you may want to hesitate before you argue vehemently I need to quit watching the weather.