Financial Analysis (1) - Introduction
Nov 7, 2015 13:44:51 GMT -5
Commish_Ron and Sean_RedsGM like this
Post by Tim_GiantsGM on Nov 7, 2015 13:44:51 GMT -5
Given that many questions about finances and budgets remain outstanding, I decided to embark on a project intended to provide some insight via the analysis of data collected over a period of time in an independent league driven by logic built into the game engine with no human interaction. Hopefully analysis of the data will support ideas previously expressed in the discussion threads. Additionally, I hope we can use the independent, historical database to pursue answers to additional questions as they arise.
Some of the questions we have pertain to areas that are difficult to quantify. One area is the effect of achieving, or failing to achieve, goals set forth by owners. Another is the effect of various combinations of personality characteristics associated with the owners. Thus, to date I have focused on data that is more easily quantifiable (e.g., won-lost record; net profit/loss; budgets).
Note that the analysis does not employ advanced statistical techniques used in formal research endeavors. Nevertheless, I hope the approach, results, and observations will add insight supported by data generated over many seasons of experience.
In this initial post I will describe the process I am using and the data I am collecting. In follow-up posts I will present results and observations. The intent of multiple posts is to enable you to respond to specific areas of investigation with your comments and feedback. My guess is that the results, observations, and comments will prompt additional questions that may be able to be addressed via additional research and additional posts.
Methodology
I began by creating an MLB universe with actual teams and rosters. Also, I used all of the default league and AI settings. Then I simmed 15 seasons with no human interaction. Next I simmed ten additional seasons with no interaction stopping at the beginning of each off-season to collect data. For each team and each year, the data I collected included:
The 10 seasons of data for 30 teams equates to 300 observations that are contained within one database, which enables me to sort and filter the data. I believe the number of observations are enough to begin to help us gain some insight into our questions.
Next Steps
In the next two posts I will examine the topics of: a) potential inflation/deflation of total budget dollars; and, b) the influence of won-lost records and net profit/loss on budgets.
Going Forward
This is one person's attempt to add insight that will help all of us going forward. If you have ideas that may help us dig deeper and learn something about the game, please jump in. I welcome feedback, suggestions, and questions.
I very curious to see what various approaches to analysis of the data might yield. I hope you find the analysis, results, and observations helpful.
Some of the questions we have pertain to areas that are difficult to quantify. One area is the effect of achieving, or failing to achieve, goals set forth by owners. Another is the effect of various combinations of personality characteristics associated with the owners. Thus, to date I have focused on data that is more easily quantifiable (e.g., won-lost record; net profit/loss; budgets).
Note that the analysis does not employ advanced statistical techniques used in formal research endeavors. Nevertheless, I hope the approach, results, and observations will add insight supported by data generated over many seasons of experience.
In this initial post I will describe the process I am using and the data I am collecting. In follow-up posts I will present results and observations. The intent of multiple posts is to enable you to respond to specific areas of investigation with your comments and feedback. My guess is that the results, observations, and comments will prompt additional questions that may be able to be addressed via additional research and additional posts.
Methodology
I began by creating an MLB universe with actual teams and rosters. Also, I used all of the default league and AI settings. Then I simmed 15 seasons with no human interaction. Next I simmed ten additional seasons with no interaction stopping at the beginning of each off-season to collect data. For each team and each year, the data I collected included:
- Games Won - from the team history page
- Games Lost - from the team history page
- Earned a Playoff Berth - from the team history page
- Net Profit/(Loss) - from the team history page (Balance) or the accounting page (Season Profit/Loss)
- Budget - budget in effect for the season just completed from any of several pages
- New Budget - budget announced for the next season from any of several pages
- Budget Increase/(Decrease) - New Budget amount minus Budget amount
- Market - market size from the finances page
- Owner Patience - from the owner page
- Owner Fiscal Personality - from the owner page
- Owner Involvement - from the owner page
- Owner Priority - from the owner page
The 10 seasons of data for 30 teams equates to 300 observations that are contained within one database, which enables me to sort and filter the data. I believe the number of observations are enough to begin to help us gain some insight into our questions.
Next Steps
In the next two posts I will examine the topics of: a) potential inflation/deflation of total budget dollars; and, b) the influence of won-lost records and net profit/loss on budgets.
Going Forward
This is one person's attempt to add insight that will help all of us going forward. If you have ideas that may help us dig deeper and learn something about the game, please jump in. I welcome feedback, suggestions, and questions.
I very curious to see what various approaches to analysis of the data might yield. I hope you find the analysis, results, and observations helpful.