When developing a sales forecast, an integrated approach is important, using several forecasting methods at the same time and comparing the results obtained. Among these methods, the most common are the following:

1) The method of expert assessments (including the opinion of a group of managers and a combination of the opinions of sales workers). This method of making a forecast is most suitable for new enterprises that do not have enough experience in using other methods. This method is also applicable when there are no detailed calculations on the state of the market, there are no complete statistics on the sales trends of certain types of products.

2) Extrapolation of trends and cycle. When using this method, errors are inevitable, but it is invariably used in sales forecasting, a low percentage of predicting the consequences of socio-economic phenomena does not contribute to high forecast accuracy. The application of this method is possible if the analyst has at his disposal massive amounts of information on various areas of the company's activities over the past 10 years.

The use of this method is based on the following techniques:

A) Definition of moving averages.

The diagram of sales of products most often has a spasmodic character. Averaging the results of observations will allow you to build a sales curve over time. A suitable number of observations are averaged. Quarters can be used, which means adding the first three results and dividing the sum by three. Then the results of the second, third and fourth observations are added up and divided by three, and so on. The result is a quarterly moving average. The constructed schedule to determine the prospective sales values.

B) Smoothing models.

Over time, more observations are made and the size of prediction errors is determined. At the same time, it seems rational to take into account past errors when predicting the future. One way is to add a fixed percentage of last month's error to last month's actual sales and use the result to predict the next month. With this method, quite good short-term forecasts can be obtained. Such forecasts are useful for production planning and inventory management, but are practically inapplicable for financial planning.

3) Forecasting on a portfolio of orders, that is, on the basis of existing or expected orders of potential buyers of products, which is preferable for the formation of sales in high-tech industries. The application of this method requires special studies on the main industries that consume the products of this enterprise, the collection and processing of significant statistical and factual material. This method is preferred in the sectors of the raw materials and energy complex, as well as in enterprises that produce components and assemblies.

4) Correlation analysis, that is, the determination of statistically significant factors influencing the sale of the company's products. With the help of the correlation relationship, the tightness of the relationship between the level of sales and the various results of the economic activity of the enterprise, the impact on sales of which can be logically proven and justified, is determined. Thus, the most significant factors are identified and ranked (according to the degree of their influence), depending on which the sales volume may change in the future. This method requires special and expensive research. The most accurate results can be obtained in the most stable economic sectors.

The effectiveness of the application of a particular method depends entirely on competitive conditions and the specifics of the economic activity of the enterprise and can only be determined in the system of general market research activities. In marketing-oriented companies, several forecasts are made using various methods (3-4 methods). The resulting estimates are then compared in order to identify emerging differences in the estimates. It is usually considered that the forecast is correct if the difference between the estimated and actual sales does not exceed 5%. If these discrepancies are significant (the spread in the values ​​of sales forecast indicators by various methods exceeds 10%), then, most likely, errors were made when compiling the sales forecast using any method.

Forecasting- activities aimed at identifying and studying possible alternatives for the future development of the company. The main role here is assigned to forecasting the sale of products. The main purpose of the forecast is to determine the trends of the factors influencing the market situation.

When forecasting, they usually distinguish short-term forecasts - for 1 - 1.5 years, medium-term - for 4-6 years and long-term - for 10-15 years.

The main emphasis on short-term forecasting is done on a quantitative and qualitative assessment of changes in production volume, supply and demand, price levels and indices, currency ratios and credit conditions. Temporary, random factors are also taken into account.

medium term and long-term forecasting based on a system of forecasts - supply and demand, restrictions on environmental protection, international trade.

As forecasting tools, formalized quantitative methods (factorial, statistical analysis, mathematical modeling), expert assessment methods based on the experience and intuition of specialists in a given product and market are used.

The most important forecasts in the activities of firms are sales forecasts, in the development of which the following main methods can be used:

• survey of a group of heads of various services and departments of the company, as well as generalization of assessments of individual sales agents of the enterprise and heads of its sales departments - the forecast is the average of their opinions. The method is used for new firms that do not have experience in using other methods, and also when there is no detailed information on market development trends. Within the framework of this method, it is possible to take into account regional characteristics of demand and conditions for the sale of the company's products;
• forecasting based on past turnover - the growth rate of sales in the reporting year compared to the previous one is determined and it is assumed that the achieved growth rates will be maintained in the next year:
  Next year's turnover = Reporting year's turnover x (Current year's turnover: Last year's turnover).
  The method is used for markets with a stable market environment, a slightly changing assortment, slight fluctuations in turnover and a sluggish scientific and technological progress;
• analysis of trends, cycles and factors affecting sales. The most significant factors include: long-term growth trends of the company, cyclical fluctuations in business activity, seasonal sales changes, technical shifts, the emergence of new competitors, etc. The method is used for long-term forecasts for a period of at least 3-5 years and is most applicable in capital-intensive activities;
• correlation analysis - complements the previous method, but is based on the use of more complex methods of statistical analysis. The tightness of the relationship between the level of sales and various factors influencing it is revealed, on the basis of which the factors are ranked according to the degree of significance. The method requires high costs associated with in-depth market research, and gives the most accurate results in markets with a stable conjuncture;
• forecasting based on the "market share" of the firm's sales Sales are projected as a percentage of the firm's market share in the industry. The company's share in total sales in the market is calculated. When using the method, it is important to be sure of the accuracy of the sales forecast for the market as a whole and not to take into account non-price competition;
• end use analysis— the forecast is based on the expected volumes of orders of the main clients of the firm. Total sales usually exceed this figure by a certain percentage. The method requires research on the main industries that consume the company's products, and is most preferable in the sectors of the raw materials and energy complex and in firms that produce finished products and assemblies;
• product range analysis- sales forecasts for individual types of products are brought together and form the planned turnover of the company. The method is suitable for diversified firms; its accuracy depends on a detailed study of the market for each type of product;
• trial marketing - one of the most accurate approaches to sales forecasting. The new product and the system of its promotion on the market (prices, types of advertising, distribution channels, type of packaging) are tested in a small regional market, and then information about the volume of sales on it is distributed to the entire sales market of the company;
• standard probability distribution methods- three types of sales forecasts are determined by expert way: O - optimistic forecast; AT - the most probable forecast; P - pessimistic assessment of the sales forecast. Next, the expected value of the sales forecast (C) is calculated using the formula

C \u003d (O + 4V + P): 6.

Standard deviation (CO) is calculated as C0 = (0 − P) : 6. In accordance with the general theory of statistics, the most probable value of the variable - sales volume with a probability of 95% will be within C ±2 SD.

The effectiveness of the application of a particular method depends on the specifics of the company. It is usually considered that the forecast is correct if the deviation of the actual turnover from the planned one is no more than 5%.

The sales forecast is the basis for drawing up a plan for the production and sale of the company's products.

In accordance with the forecasts compiled by the CES, the energy needs of the countries of the common market for the period 1970-1985. will more than double - up to about 1.8-2.0 billion tons in terms of standard fuel, and the average annual increase will be about 5%. According to estimates, the share of industry in total consumption will be 55%, the household sector - 32% and transport - 13%. The structure of the fuel balance of the countries of the common market by 1985 will be as follows (in%) oil - 65 natural gas - 15 solid fuel - 9 nuclear and hydropower - 11.

According to the degree of coverage of AIT management tasks, electronic data processing is distinguished, when, using a computer, without revising the methodology and organization of management processes, data is processed with the solution of individual economic problems, and management automation. In the second case, computing facilities, including supercomputers and PCs, are used for the integrated solution of functional problems, the formation of regular reporting and work in the information and reference mode for the preparation of management decisions. This group can also include AIT decision support, which provide for the widespread use of models and PPP for analytical work and the formation of forecasts, drawing up business plans, reasonable estimates and conclusions on the processes under study, phenomena of arbitrary economic practice. This group also includes shi-

A. Check that prices are close to the forecast made for the Fifth Wave

It is much easier to program time-aligned bars, but they are not as good for analysis as market-aligned bars. Let's take bonds as an example. Even though the bond market opens at 8:20 am, effectively ending its first half hour at 8:50, the time-aligned bars would start measuring this market at 8:00 am, ending the first bar at 8:30. In this case, the first 1/ 2 hours (8 00-8 30) will include only 10 minutes of actual data coming from the market. The second half-hour bar will contain information for only 20 minutes of the first half of the hour and 10 minutes of the second half of the trading hour. Another example of time-aligned bars that make “false” highs, lows, and last data would be the hourly S P. In this case, the first hourly S P bar contains information from 9:00 am to 10:00 am, although it does not start until 930 The second hour starts at 1000, ending at 1100 in the morning, instead of the correct start at 1030 and ending at 1130 in the morning. Obviously, if the highs, lows, and latest data for these intraday charts are "incorrect", all forecasts made from them are also incorrect. Don't let the feeling of satisfaction blind you. Some traders have been using time-aligned bar calculations for years with below average results. Many of these traders have absolutely no idea how these predictions are made. I assure you that the poor performance of indicators may be more the result of inappropriate data on which they are calculated, rather than the imperfections of the indicators themselves or the trader's misunderstanding of the rules for their use.

This forecast is based on data from the Ministry of Economy of the Russian Federation, forecast

Consequently, the unanimity of the majority of experts is not always a criterion for the reliability of assessments. Hence the need for careful selection of experts. The fact is that when discussing many issues, especially non-standard ones, for example, forecasting the market situation in unstable political and economic conditions, highly qualified experts should participate. Forecasts made by average experts will be based at best on traditional, habitual estimates, while highly qualified specialists will discover and evaluate hidden factors.

For example, manufacturers of baby food may think that their sales depend on the birth rate (do they). Sales of excavators depend on the volume of housing construction. Thus, it is necessary to collect data on sectors/industries related to a particular product and analyze the correlation of this information with data on its sales. The disadvantage is that you can use the wrong relationship or several different indicators at the same time. However, this method is useful for explaining some trends and even for testing forecasts made using time series or subjective estimates.

Factors taken into account in a forecast based on experience should change not only under the influence of trends that arise over a certain period of time, but also as a result of an assessment of general business conditions. This is reflected in receipts, thoughtful changes in the terms of sale or credit policy. There are preconditions for improving this approach if there are significant differences in practice between geographic regions, consumers or distribution channels.

However, some marketers criticize the reliability of forecasts based on the results of such marketing experiments. The main reason for their negative attitude is that the behavioral characteristics of the buyer and his choice are largely influenced by the environment in which the purchase is made. In this case, it is far from real.

The table shows the full sales forecast that many companies use in budgeting. Note that first quarter sales are detailed to show expected sales in each of the three months. The forecast (sometimes called the sales budget) discloses the expected sales by product and by region. This overall forecast is a composite forecast based on sales estimates for each area where products are sold.

Another disadvantage of the method is manifested in large companies. So, if at each level of the combined forecast (i.e., at the level of sales agents, regional managers) forecasts are made with an excessive number of random indicators (for example, underestimation), then the combined forecast, compiled by the top sales manager, may be essentially useless. This disadvantage can be overcome by determining the degree of randomness of the forecast results and using a correction based on past results, or by incentivizing all sales agents to make more realistic forecasts.

In most cases, the forecast, developed on the basis of need, determines the upper limit of the necessary development. The lower limit is set by the forecast calculated on the basis of the descriptive approach, and the middle one by the forecast compiled by developing a program to fill the "gap".

Thus, observations of relative levels of inflation, or of the causes of it (such as differences in the rate of expansion of the money supply) can be used to predict changes in exchange rates. However, this approach is not suitable for short-term exchange rate projections. Rates may differ from forecasts based on parity

From the analysis of Fig. 4.3(6) it follows that, according to the optimistic forecast made by our financial manager , the costs of the financial transaction (i.e. potential losses), which are formed at the moment t=0, should pay off at the time t will end because, according to this optimistic forecast, the required level of profit will be reached. Similar points in time for the pessimistic forecast of the course of the process of profit formation are indicated in Fig. 4.3(6) through TJ and respectively. We will continue to mark the real trajectory of the process of changing profits in the course of a financial transaction in our figure with bold arrows. Articulation points of the arrows in fig. 4.3(6) denote the moments of making current management decisions that change the direction of the flow of the real process.

Forecasting with a genius. This method is based on the idea of ​​finding a genius and getting an intuitive forecast from him. This method precludes the use of rational and precise methods. A forecast made by a genius cannot be verified, which is fraught with big troubles for an enterprise (firm) operating in market conditions.

The main difference is the use of various methods of financial analysis in the evaluation of investment projects and business (cash flow calculation, present value calculation, risk assessment, etc.), since accounting methods do not provide an adequate description of ongoing and expected processes in the future. However, the use of financial analysis methods is often not possible without the use of accounting documentation, relevant forecasts, compiled as part of the financial planning of the enterprise.

The directive documents of the CPSU developed a set of measures for the further improvement of management. In this aspect, the development of economic reform on the basis of new principles of planning and economic stimulation of the development of social production is of particular importance; strengthening the work on compiling socio-economic forecasts for the long term, on the correct combination of territorial and sectoral planning; improving the organizational structure of management from the bottom to the top; strengthening state discipline. in all links of the national economy, the widespread use of modern computer technology and economic and mathematical methods, and the ever broader involvement of the working people in management. At this stage, the implementation of

Proportion of currency exchange of different countries. For most currencies of the world, these rates fluctuate constantly, reflecting the changing situation in the foreign exchange markets. Fluctuations in exchange rates directly affect the entire development of world trade, since the degree of profitability of buying or selling goods in the markets of various countries changes. Therefore, in order to make a decision on the expediency of concluding a foreign trade contract with a firm of a particular country, it is advisable to use information about forecasts of changes in exchange rates by the time the contract is executed and payments are made. Dozens of companies all over the world are engaged in making such forecasts for banks conducting currency transactions in the foreign exchange markets, as well as commercial firms.

The methods under consideration are used in long-term and current planning, but they may not be enough when making long-term forecasts, when it is not yet known exactly what equipment, technology, forms of production organization will be. Therefore, when forecasting, several more methods are additionally used.

Relevant experts have developed several specific methods for compiling and improving the quality of forecasts. In table. 8.2. briefly describes the main types of forecasts often used in conjunction with the planning of the organization's activities. The results of forecasting are included in the goals of the organization, determined by management.

In the process of compiling the program for the development of the French economy, forecasts were developed covering the period up to 1985. In Table. 16-V provides a forecast of the energy balance of France until 1985.

However, after this forecast was made, the demand for energy already in 1968 reached the level assumed for 1970. This fact caused the need to amend the energy forecast. The average annual growth rate of gross national income according to the revised plan in 1969-1975. and in 1976-1985. were planned in the amounts of 10.6 and 8.5-9.5%, respectively, and the indices of industrial production compared with the base year of 1968 were estimated in 1975 and 1985. 392 and 945-1074, respectively. respectively, will be 438 million m3 and 933-1029 million m3, i.e., it will increase in comparison with 1968 by 2.1 and 4.4-4.8 times (Table 28-V).

This can be seen from a comparison of reality with forecasts made by such well-known organizations as the RAND Corporation, the 2000 Commission, Resources for the Future, the Hudson Institute, and a number of bourgeois scientists.

Statistical data that can be useful as industry data over time have already been mentioned in this book. Chapter 6 discusses intercompany comparisons using information from Robert Morris Associates. Given fluctuations in cash flows over a sufficiently long period of time, including the full economic cycle in the industry, it is possible to calculate the standard deviation of cash flows. It is then easy to calculate, for each industry, a coefficient of variation , which, given a specific confidence interval , will allow one to calculate the probability of a shortage of funds compared to the analyst's forecast based on the methods described in Chapters 7 and 8.

Let us now present the forecasts of world gas consumption. As well as forecasts of energy consumption in general, they also vary within a fairly wide range. For example, the IIASA Report shows a fork of forecasts that is several times higher than the minimum forecast, which shows significant uncertainty in long-term forecasts. The discrepancy between forecast options compiled by different organizations can be quite significant. This is mainly due to different understandings of high and low scenarios, the inputs for which are not always specified. The range in which there is a discrepancy between the predicted values ​​can reach several hundred percent. This once again testifies to the need to create a "transparent" forecasting system that allows flexible consideration of various scenarios of global or regional development.

The key to such a forecast is the indicators of the future dynamics of the company as a whole, and among them the most important are estimates of sales volumes and net profit margins (a ratio expressing the ratio of net profit and gross revenue. - Approx. scientific ed.). One way to build a sales forecast is to assume that the company will perform the same as it did in the past, and so you just need to extrapolate from its past trends. For example, if in the past the sales volumes of a company grew by 10% annually, with this approach to forecasting, it can be assumed that in the future they will grow at this rate. Of course, if there is any economic or industry data that the company's sales volumes will grow faster or, conversely, slower than in the past, the forecast should be adjusted. It is likely that this naive, simplistic approach to forecasting will be just as effective as forecasts based on more sophisticated methods. As a rule, the sales forecast covers a period of one to three years. An attempt to extend it beyond these limits leads to an increase in uncertainty and sharply reduces the quality of the forecast.

By chaining managers with golden chains, Jenin is able to create the tension that drives the firm forward. "The key to the system," explains one of the company's managers, "is the profit forecast." Once the forecast is made, reviewed and agreed upon, the manager must report to Jen-nin on its implementation. This is how the tension arises, on which success depends. Tension permeates the whole company, causing ambition, perhaps even excitement, but always tinged with fear of what will happen if the goal is not achieved 14

Forecasts are useful for planning and implementing changes that experience-based forecasting of future business operations only if it becomes useless Do data on similar, if components of the forecast are carefully considered, products (or development options) justify and limit the forecast , open to make a prediction about the fate of your produh-veins but named. and How easy or inexpensive it will be There are several ways to do this to obtain reliable information about the experience of the past                         Marketing Management (1998) -- [

Making a forecast

Proof of validity by a criterion means that the results of the test can be used to draw conclusions in the form of predictions. Therefore, the basic procedure used to collect this evidence is called forecasting ( predictive design).The correlation coefficient is calculated between the test results and the grades subsequently given by the same test subject according to some criterion. It is this procedure that was used in the example graphically displayed in Fig. 3.3: a correlation was found between the results of an arithmetic test obtained before admission to training, and marks given by managers after two weeks of vocational training.

Making a prediction has traditionally been considered the preferred way to obtain evidence of validity for a criterion, but its practical application has certain disadvantages. The main one is related to the presence of a normal distribution of subjects across the entire scale of test scores. For the correct use of the predictive scheme, it is necessary that the range of results of the subjects from the sample used in the validation of the test be complete. Therefore, it is necessary to hire several candidates with low test scores. It is rather difficult to convince employers of the need for this requirement. If the test is to be used to select for a job, it's natural to assume that people who get low test scores won't be able to do a good job - so why hire them?

Another possible problem with criterion validity prediction is that there is some time between the collection of test data (predictor variable) and the collection of criterion data. As behavioral predictions are extended to a more distant future, their accuracy decreases significantly (Henry & Hulin, 1987; Hulin, Henry & Noon, 1990). Supervisory ratings, which seem to be the most commonly used criteria in such studies, can be particularly affected by this problem because they are made at a specific point in time and relate to the performance of a particular job. One way to get around this problem is to use a parallel (concurrent) schemes for proving validity by criterion.

Make sports betting without preliminary analysis and forecasting the outcome, perhaps the strangest decision of bookmaker players. In order to get a positive result over a long distance, you must predict the outcome of the meeting, and only then make a decision to bet on it or not. In this article, we will share practical tips, tell you how to properly analyze information and make predictions for matches, as well as give a clear algorithm of actions.

Predicting sports matches is not an easy task. The forecasting itself is a continuous analytics. In addition to the fact that you will need to process a huge flow of information, you also need to be able to sort it by importance, as well as "sort it into pieces". This is comparable to a large library. A huge number of books have been brought to you, you must sort by genre, author, significance (after all, uninteresting works can be removed away), or by some other features and parameters, put everything on shelves, and when you are asked to give out a book, in a few seconds you have to figure out where it is. It is the same with forecasting, first you read and receive information, and then at any moment you will have to pull it out of your head and apply it correctly. Such information can be statistics, injuries, statements by coaches and players, team goals for the season and for a particular match, motivation, and many others.

Before you start, it is worth recalling that the primary and one of the most important tasks of a player is the right choice of a bookmaker. Bet only in the best bookmakers! You can read about how to find a decent bookmaker. Well, we advise you to pay attention to three offices from our rating: BC "WINLINE", BC "MELBET" and BC "1XBET". These are high-quality and reliable betting operators that do not create problems for players, and this is the most important thing. Plus, in these bookmakers you will find a lot of events for betting, a wide range and excellent odds.

Selecting a sport and league to predict

First of all, you should choose a sport that you are well versed in. If you love football and hockey, you know the players, teams, the various subtleties of these sports (and if you don’t know anything, then it’s not clear why you bet at all), then it makes no sense to climb, for example, tennis or basketball. Of course, it is best to specialize in one sport, but predicting two is, in principle, uncritical. In addition, you need to weed out those leagues and championships that you do not know. For example, if you predict football matches, then you need to choose the top leagues (the Premier League, Bundesliga, Premier League, Primera, etc.), and not climb into the third division of Zimbabwe or the fourth league of Germany. Predicting hockey matches - choose the KHL and NHL, predicting basketball - choose the NBA, etc. Most importantly - do not jump from one sport to another, and do not break into several championships. If you are experiencing difficulties and feel that, frankly, do not pull on several sports, championships, leagues, then it is better to reduce their number.

Forecasting includes four phases:

1. Collection of all kinds of information;
2. Processing and sorting information;
3. Analysis and preparation of a preliminary forecast;
4. Predicting the outcome of a match

Collection of information about the match

Statistics

At the first stage, you need to collect all the information about the match you need to make a prediction. First of all, study the statistics. Do not forget to divide all indicators into away, home and general.

The most important indicators:

• last meetings of the teams;
• last face-to-face meetings of rivals;
• tournament position;
• average value of goals scored per match;
• average value of goals conceded per match;
• average of goals scored in the last five matches;
• average of goals conceded over the last five matches.

Thanks to the statistics of the last meetings, you can determine the current form of the team (players), scored / missed - from the same opera, only you can find out more about the performance at any segments of the season. Based on the history of face-to-face confrontations, a number of indicators can be determined, for example, the effectiveness of teams in a game with each other, how often opponents hit each other's goal, etc. According to the team's standings, you can determine the general indicator of the game form (for a certain number of matches or for the season as a whole), as well as learn about the motivation and tasks of the team for the next game.

Lineups

Usually lineups become known an hour and a half before the start of the match, but according to information about injuries, you can have a rough idea of ​​the lineups for the upcoming match. You can also compare lineups, say, for the last three matches of the team.

Series

Highlight different streaks: wins in a row, losses in a row, goals scored in a row, goals conceded in a row, shutouts and meetings on "both to score". These are the most important indicators in forecasting.

News

Read the press, special attention should be paid to interesting facts, rather than the statements of players and coaches. An example of an interesting fact: “Lokomotiv” visiting “CSKA” cannot win in ten matches in a row. Here, an interesting fact is immediately evident, and statistics, and a series, in this case without victories. Such information always needs to be noticed, processed and applied.

Motivation

One of the most important factors is motivation. You must accurately find any motivational components. For example, the last round of the Champions League group stage is underway. Real Madrid are third in the group, two points behind Borussia Mönchengladbach, who are in second. The next match at the “Real” just with the “Borussia”, and at the “Santiago Bernabeu”. Obviously, the "Royal Club" sets itself the task of reaching the playoffs of the Champions League, and not getting into the Europa League, so the team will enter the match, which will also be home, with special emotions.

Rematches

The thirst for revenge is another of the most important factors that can directly affect the result of the match (in the case of approximately equal opponents). It is not uncommon for a team to rehabilitate itself in front of their fans after a major home defeat. Lost advantage and defeat in the last seconds, defeat in overtime, on shootouts, defeat due to a goal that was not counted according to the rules, defeat in the derby - all these situations give rise to a desire to take revenge from the opponent.

Derby

Separately, it is necessary to touch on the derby teams. Derby is a competition between teams from the same city or region. Opponents usually go to such matches over motivated, because the victory in the derby is doubly sweet, in addition, it very often promises good bonuses.

Information processing and sorting

When you have received a sufficient amount of information about the match, you need to immediately discard everything superfluous, and superfluous is something that will in no way affect the outcome of the match (garbage). Next, you need to structure the information, separating important factors from secondary ones. All factors are divided into direct, indirect and abstract.

Direct factors

direct factor- this is a factor that can directly affect the outcome of the meeting. For example, an injury to the leading player of Team 1.

Indirect factors

Indirect factor- this is a factor that can only indirectly affect the outcome of the meeting. For example, the appointment of a referee for a match, who does not skimp on red cards. Because of this, there is a high probability of a Team 1 player being sent off in the match, as a result of which Team 1 may concede, fail to score (in case of early removal), lose. The current weather, intra-team intrigues, disagreements, etc. are all indirect factors.

abstract factors

abstract factors- these are events during the match that cannot be foreseen in any way. For example, a player's injury during a match, worsening weather (rain, snow), fan noise, good luck and bad luck. Please note that it is the worsening weather, and not the current weather. The deterioration of the weather is an abstract factor, the current weather is indirect, but in combination with others it can also become direct. An example of failure: a player goes one on one with the goalkeeper, shoots and he falls into the post. An example of luck: a player shoots at the goal, a player appears in the path of the ball, and the ball after the rebound ends up in the net. Note that for one team the same hit on the post is bad luck, but for the other it will be good luck. Abstract factors should simply be neglected. Firstly, it is almost impossible to predict them, and secondly, in the case of successes and failures, we believe that there will be approximately an equal number of them in the match, and they compensate each other.

For us, the most important will be direct factors, and indirect ones only selectively. Why selectively? Yes, because an indirect factor in combination with other indirect factors and under certain conditions can become direct. If we take into account every little thing and calculate the probability of an event that was generated by another event, we will go crazy. You don't even have to go to the point of being idiotic.

How to make a preliminary prediction for a match?

Further, we work only with direct factors (statistics, injuries, etc.). We collect everything that happened and analyze. At this stage, you must choose the event in the match (bet) that you think should happen. Forget about calculating the probability of passing a bet or calculating the probability of an event occurring! In this case, you are interested in the following: will this event happen or not happen, 1 or 0, false or true. Naturally, the bet should be supported by something, and not taken at random. This is where you need to use that "principle of the library", pull out small pieces and start assembling the puzzle. You should be able not only to make a forecast, but also to explain your choice in favor of this rate.

How to Calculate Team Strength Rating

When making a preliminary forecast, you can use a very good system for calculating the strength rating of teams proposed by J. Miller. Ideally, it was written for American football games, but we tweaked it for football, hockey, and basketball. However, this system works better in high performance sports (volleyball, basketball, handball, American football, etc.).

First of all, you need to remember two main rules:

1. Use this system only after 5-6 championship rounds have been played;
2. Do not use the results of friendly and pre-season matches.

You take the last 5 meetings of the team and write down the goals scored, pucks, etc. Cross out the highest and lowest performance in terms of goals scored. Then add up the remaining three numbers and divide by three. In order to calculate the defense rating, do the same: cross out the highest and lowest value of goals conceded, what is left, add and divide by three.

Example

Detroit Red Wings vs. Pittsburgh Penguins of the National Hockey League.

Detroit's last five meetings:

Detroit 1-2 Anaheim
Detroit 3-4 Vancouver
Detroit 5-1 San Jose
Detroit 3-1 Florida
Detroit 3-2 Edmonton

Pittsburgh's last five meetings:

Pittsburgh 1-3 Buffalo
Pittsburgh 3-2 Arizona
Pittsburgh 6-1 Toronto
Pittsburgh 2-3 Montreal
Pittsburgh 3-1 New Jersey

Goals scored by Detroit: 1, 3, 5, 3, 3.
Missed washers "Detroit": 2, 4, 1, 1, 2.

Goals scored by Pittsburgh: 1, 3, 6, 2, 3.
Missed pucks "Pittsburgh": 3, 2, 1, 3, 1.

We cross out 1 and 5 goals (the smallest and the largest performance indicator), and add the remaining three indicators: 3+3+3=9. Now we divide this value by three: 9 \ 3 \u003d 3.

We cross out 1 and 4 goals (the smallest and the largest indicator of goals conceded), and add the remaining three indicators: 1 + 2 + 2 = 5. Now we divide this value by three: 5 \ 3 \u003d 1.66.

We cross out 1 and 6 goals (the smallest and the largest performance indicator), and add the remaining three indicators: 3+2+3=8. Now we divide this value by three: 8 \ 3 \u003d 2.66.

We cross out 1 and 3 goals (the smallest and the largest indicator of goals conceded), and add the remaining three indicators: 3+2+1=6. Now we divide this value by three: 6 \ 3 \u003d 2.

How to make a prediction for the exact score of the match?

The strength rating can be used to determine the preliminary score of the meeting between the two teams. Let's try to estimate how many points Detroit can score, and which Pittsburgh. To do this, add Detroit's attack rating to Pittsburgh's defense rating, and then subtract 3 (the average of team points in the NHL).

Average total for leagues and sports:

• NHL = 3
• KHL = 2
• NFL = 20
• NBA = 100
• Football = 1

Calculation:

"Detroit": 3+2-3 = 2
"Pittsburgh": 2.66+1.66-3 = 1.32

For home teams, you should almost always round up the value, or add 0.5 or 1, but you need to look at the average number of goals conceded by the opponent in away matches. If the away team concedes quite a lot, feel free to round up. You can also calculate the average value of goals scored / conceded for the season, or for a segment of at least 10 matches. In our example, we will round both values ​​up. Let's say that Detroit has a 2.66/2.53 goals/conceits record at home, and Pittsburgh has 2.71/2.55 goals away. “Pittsburgh” misses more than 2.5 washers on the road per game, so we increase the value of the goals scored by “Detroit”. Likewise, Detroit concedes more than 2.5 at home, while Pittsburgh has a high conversion rate on the road - 2.71 on average per game.

Thus, we calculated that the match "Detroit" - "Pittsburgh" can end with a score 2-2 or 3-2 (Boundary value). But now you need to start from additional information and indirect factors, which may affect the outcome of the meeting.

How to make a final match prediction?

First we have to check again. See if you have correctly interpreted the information you received, did you correctly separate direct and indirect factors if you missed anything important. Are you right calculated the strength rating of the teams. Further check if there is new information, and if it appeared, then you need to take it into account when making a forecast. Now analyze whether a combination of indirect factors will affect the successful passage of the bet. For example, if there are a lot of them, see if they have something in common. Wouldn't it be possible that one indirect factor together with another will give rise to a direct one? In any case, all this must be taken into account, but do not confuse yourself. Just analyze, ask yourself questions and answer them. You are just thinking, and confusion will not lead to anything good. You will end up simply entangled in your own thoughts and doubts.

This is how predictions for matches work. Remember that each match and each case is unique, so your situation may differ from those given. The most important thing, as already mentioned, is to collect information, discard the excess, analyze it and make a forecast, and then check yourself. Your main tool is statistics and news. Think and analyze, but don't get carried away. You need to analyze deeply, but quickly, otherwise you will drown in your own thoughts. Good luck forecasting!