Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the US Powerball lottery. Please sign up to access the interactive Powerball HTML graph posts listed under the Powerball tag, which offer selectable options and much more content. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference

Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the US Mega Millions lottery. Please sign up to access the interactive Mega Millions HTML graph posts listed under the MegaMillions tag, which offer selectable options and much more content. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference

Introduction

In this post, we explore lottery winning data from various perspectives. We analyze the history of lottery numbers to uncover patterns and insights.

While it's often assumed that lottery numbers are purely random, the reality is that different authorities use various computerized algorithms or machines that may impact randomness. For the sake of this discussion, we'll treat lottery winning numbers as purely random.

We'll use Canada's BC49 lottery as an example. BC49 is a 6/49 jackpot lottery, and our analysis focuses on the six regular drawn numbers, excluding the bonus number. We refer to a regular drawn number as a "ball."

Some graphs in this post are toggleable and interactive, allowing readers to toggle to full screen and select different options for draws and numbers, and to zoom in on specific graph zones.

Lottery Numbers in a 3D Coordinate System

At GameSeer, we visualize lottery winning numbers in a three-dimensional coordinate system. Our pioneering research aims to explore analytics in 3D.

Let's take the recent five draws of BC49 data as an example:

This set of lottery data can be mapped into a 3D coordinate system as follows.

Right click on the graph and move mouse around to view it in different angles:

Each lottery ball has 3 coordinates. Specifically:

• The x-coordinate: Represents the difference of the same order ball in adjacent draws (denoted as Bx).
• The y-coordinate: Represents the difference between adjacent balls in the same draw (denoted as By).
• The z-coordinate: Represents the ball's number itself in the draw (denoted as Bz).

In the above 3D graph, the adjacent ball difference in a draw is denoted for balls 1 to 6 as By1 to By6. Their values are: By1 = 49 - Bz6 + Bz1, By2 = Bz2 - Bz1, By3 = Bz3 - Bz2, By4 = Bz4 - Bz3, By5 = Bz5 - Bz4, By6 = Bz6 - Bz5. By1 is a loop-roundup difference from By6, while other By values are simply the difference from the prior order ball.

Typically, people only consider the ball number itself (Bz data) for lottery analytics. However, all three coordinates exhibit the same mathematical or statistical properties and should be analyzed similarly. Analyzing all three coordinates increases the possibility of improving accuracy.

Multi-layer Coordinate System Mapping

We refer to the above differentiation of views for lottery data as data mapping. This data mapping can have multiple layers. For example, the ball-difference/y-coordinate of a specific draw (x scale = i) are: By6(i), By5(i), By4(i), By3(i), By2(i), By1(i). This set of six values and its adjacent next draw value By6(i+1), By5(i+1), By4(i+1), By3(i+1), By2(i+1), By1(i+1) can form a second layer of draw difference mapping. To simplify, we will only discuss the first layer of such data mapping; the principles apply to other deeper layers.

Lottery Number and Data in GameSeer Graphs

Currently, our interactive graphs process and display one dimension of the lottery data, primarily showing the ball number itself (Bz data) with a total of 14 types of graphs. We can also offer the same for Bx and By, but currently, we only have three graphs for each as examples. In our discussion, we focus on one dimension, mainly ball numbers and their statistical data.

The most common graph for lottery data is the lottery ball number history in a bar style. The following graph is a stacked bar for all balls. You can enable or disable one or more balls.

Lottery Numbers in Binary form

At GameSeer, we also view lottery winning numbers' one-dimensional data in multiple numeral systems, not just decimal.

The following shows the lossless binary mapping data from the original data set.

Drawing from the history of signal processing, we know that digitization for audio signals shows PCM signals are more robust and tolerant for transmission and storage than analogue signal. In the real world, it's harder to deteriorate from 1 to 0 than from 49 to 48.

For lottery data analytics, we can also "digitize" lottery data by mapping six ball decimal numbers to 49 binary numbers. To forecast, a machine can try to guess 49 times of "1 or 0" instead of six times of "1 or 2 or ... 48 or 49". This approach introduces an additional option beyond the conventional method of directly forecasting using the original decimal numbers.

Lottery Numbers in Bitmap Form

Lottery history data as two-dimensional binary data forms a bitmap, allowing the use of advanced algorithms and machine learning methods developed from 2D image processing fields.

We provide a 2D bitmap graph for each lottery type, allowing customers to evaluate the visual pattern appearance and distributions.

Viewing the bitmap form data, you can see it resembles a net with knots and black holes. Extending the time axis longer makes it more apparent that the knots and holes are evenly distributed.

Even from the human eye, you can sense some number areas are currently in knot zones or hole zones, helping to predict future patterns. We believe future algorithms, deep neural networks, and machine learning models can detect visual patterns better than human eyes.

You can also compare the lottery number bitmap with the following picture. Applying Poisson distribution analysis to lottery number distribution could be interesting.

Lottery Data in Other Domains

Time series random data, including lottery history numbers, can be transformed into other domains from its original time domain. The most used domain is the frequency domain. Similar to other popular FFT transformation applications, occurrence frequency processing and statistics are very helpful in lottery data analytics. Therefore, we provide several frequency domain graphs.

The following is an example graph in the frequency domain for a long-term period.

Unlike the number history graph, the occurrence frequency graph's x-axis is ball numbers, ranging from 1 to 49, instead of the time domain draw sequence.

The chance of appearance for each number from 1 to 49 in a draw is 6/49 = 0.12245. In the long run, each number's actual presence will be approximate 0.12245 times the number of draws. The above graph shows results from 3000 draws, and you can see each number fluctuates slightly around 0.12245 x 3000 = 367 times.

You can select more options for 2000 draws, 1000 draws, etc. The shorter the period, the more fluctuation in appearance. You can even compare them with the short-term period as follows.

Since the long-term appearance chance is constrained to 6/49 = 0.12245, you can expect nature to compensate for a number that hasn't shown up recently.

Conversely, if a number has appeared frequently, it may not show up as often in the future. However, nature doesn't rush; just because a number hasn't appeared recently doesn't mean it will appear immediately. Instead, nature balances out over time.

Mid-term statistics (50 to 200 draws) are more useful than long-term statistics as they can identify the current draw's period type, if it's in an occurrence monsoon summer or in dry winter.

If a specific number's occurrence is low in the short term (recent 5 or 10 draws), don't rush to judge it will appear in the next draws; check its mid-term season and tendency as well.

Ideal analytics should evolve beyond the traditional short, mid, and long-term periods, embracing more nuanced timeframes. Within each historical term, distinct weights should be assigned to current predictions—favoring recent data with greater emphasis. Algorithms should explicitly account for this temporal factor in their analytical models.

Lottery Numbers Occurrence History Data

GameSeer provides another category of graphs related to occurrence: lottery number occurrence history data graphs.

Unlike frequency domain occurrence data, lottery number occurrence history data is in the time domain, collecting the number occurrence counts in a series of time windows to form a statistical result over time.

In the following graph, the x-axis represents a 5-draw slot sequence. Every five draws, we count a specific number's occurrence and present it on the y-axis for this time slot.

Another type of graph differs slightly. Its x-axis is the draw sequence, similar to other ordinary history graphs. The y-axis count for each draw is the summary of occurrences traced back 10 draws from this draw. It's a convolutional 10-draw count value based on each draw.

The occurrence history data graph is important and intuitive for understanding the recent tendency of a specific ball number's appearance.

Lottery Statistical Data Distribution Patterns

The statistical distribution patterns for lottery data are the most interesting and important for analytics.

The applied manual rules, such as the upper limit of 49 on the random number of six balls, demonstrate some fascinating patterns.

Our previous experience in the telecommunication transmission field links our thoughts on lottery data patterns to Rayleigh fading over radio channels.

The upper limit of the radio signal over space is the source power. The signal deteriorates and fades during transmission, but not smoothly. Instead, it exhibits a pattern where it mostly tries to stay near the upper limit but occasionally falls into deep fades. The average SNR results from many high points and few deep lows.

This situation is similar to edge ball number distribution for the lottery.

If you transform the above diagram's time domain data into the frequency domain, the shape of the occurrence distribution resembles a Poisson distribution curve, especially for λ = 1.

Now let's see what the lottery number occurrence distribution shows.

This beautiful butterfly shape distribution reveals that, in most cases, the ball-6 appears around 43, and the distribution is not even—cases near 49 are much more frequent than those around 40 and below.

This pattern is similar to fading radio signals over a wireless channel. The occurrence of deep fades is much lower than the tendency to stay close to the upper limit.

You can find a similar pattern on the lower end. Instead of seeing even edge numbers from 1 to 14, the ball-1 numbers over 7 is lower, and numbers close to 1 are much higher.

Apart from edge balls, inner balls also display the same Poisson distribution patterns, with pair ball-2/5 have higher λ than ball-1/6, and ball-3/4 have higher λ than ball-2/5.

This distribution pattern for edge and inner ball numbers should be explored further in analysis and analytics on lottery data.

Don't Lay Eggs in All Brackets

Nature exerts pressure to keep the ball bouncing around its defined neutral mean value. Short-term vibrations always exist, and their strength should be measured. This creates a butterfly wing shape in the occurrence frequency domain, which is consistent across similar jackpot lotteries.

While the macro view of time domains over the long run appears evenly distributed, it is shaped by micro fluctuations in the short term, so it's unwise to evenly distribute numbers when filling out a lottery form.

Review the following lottery ticket and see if you can identify where the numbers are incorrectly placed:

Now, refer to the number range graph provided by GameSeer. You can select a square zone and zoom in to examine it closely:

Historical data from the graph shows that in any given draw, the 6 balls rarely fall into all the ranges of 1-9, 10-19, 20-29, 30-39, and 40-49 simultaneously. Instead, a single range often contains 2 balls, sometimes 3, and even 4 balls regularly!

You should never try to spread your 6 eggs evenly across 5 brackets.

Now it's time to dive deeper into the 20 types of lottery data graphs. You'll discover patterns, rules, and facts shaped by nature for the lottery. While this won't guarantee you can guess the 6 winning numbers, using the correct analytics can enhance your chances of hitting a jackpot number.

Welcome to GameSeer's TSRN and lottery graph wonderland!

Introduction

Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed for sequential data. Unlike traditional feed-forward neural networks, RNNs have connections that form directed cycles, enabling them to maintain a form of memory of previous inputs. This makes them particularly well-suited for tasks where the order of data is crucial, such as language modeling, speech recognition, and time series prediction.

Long Short-Term Memory (LSTM) networks are a special kind of RNN, capable of learning long-term dependencies. They were introduced to address the vanishing gradient problem that standard RNNs suffer from, which makes it difficult to learn and retain long-term information. LSTM networks utilize memory cells that can maintain information in memory for extended periods. These cells contain gates that regulate the flow of information, allowing the network to decide which information to keep and which to discard.

LSTM networks have been highly successful in various time series data applications due to their ability to remember long sequences of data. In the financial industry, they are used for stock price prediction, algorithmic trading, and risk management. In healthcare, LSTMs analyze patient data for disease prediction and treatment recommendations. In predictive maintenance, LSTMs analyze equipment sensor data to forecast potential failures, enabling proactive maintenance and reducing downtime, while in transportation, they are used for traffic flow prediction and route optimization.

In the service industry, LSTM networks have significantly improved customer experience and operational efficiency. Customer service centers use LSTMs to predict customer inquiries and optimize response strategies. Retailers employ LSTMs for demand forecasting and inventory management. Moreover, in marketing, LSTMs help in understanding customer behavior and personalizing recommendations, leading to better customer retention and satisfaction.

Despite their widespread success, LSTM networks have notable shortcomings in certain areas, particularly in some time series random number analytics, such as lottery prediction. The main limitation lies in the nature of the data itself. Lottery numbers are inherently random, and the patterns that LSTMs excel at identifying are not present in truly random sequences. This makes it exceedingly difficult for LSTMs to provide meaningful predictions in this context.

Additionally, LSTMs require a substantial amount of data to train effectively. In scenarios involving random numbers, the data does not follow predictable patterns, leading to overfitting and poor generalization on new data. The stochastic nature of lottery draws defies the core strength of LSTMs, which is to capture temporal dependencies and trends. As a result, while LSTMs can model complex dependencies in sequential data, their performance in predicting random numbers remains fundamentally constrained by the unpredictability and lack of patterns in the data.

Despite these challenges, LSTM remains a popular tool for intelligent analytics in time series random number and lottery data. Many researchers and practitioners continue to experiment with LSTM networks to uncover potential predictive power in these datasets. At the end of this post, there are references to several articles and projects that document these attempts and tests.

To address the current LSTM issues with lottery history numbers, we at GameSeer tried new data preparation approaches to improve the efficiency and accuracy of LSTM predictions.

LSTM Example Codes

LSTM implementations are widely used in many Python projects. You can also find numerous Jupyter notebooks for testing and evaluating LSTM on time series random data.

The following is part of an example of such implementations.

LSTM is a machine learning model that requires a defined working profile containing many factors to perform training (model.fit) and prediction (model.predict). If you are interested in understanding the function and algorithm selections for the model, you can study a series of articles posted by Dr. Jason Brownlee on machinelearningmastery.com.

Data Preparation into Binary Form

Based on existing experiments, we aim to optimize our solution by altering the dataset preparation method. The main idea is to transform the lottery decimal numbers into different domains and numerical systems, revealing more statistical and sequential patterns that can be recognized and processed by AI models, deep neural networks, and algorithms.

Currently, we map decimal ball values to binary data, creating a 2D binary map with 49, 69, or 70 columns/features, depending on the lottery type. By applying LSTM to the binary form of the original decimal data, we observe different forecasting results.

We take the recent Canada BC49 lottery history numbers as an example. The following shows the lossless binary mapping data from the original data set.

Drawing from the history of signal processing, we know that digitization for audio signals shows PCM signals are more robust and tolerant for transmission and storage than analogue signal. In the real world, it's harder to deteriorate from 1 to 0 than from 49 to 48.

For lottery data analytics, we can also "digitize" the lottery data by mapping the six decimal numbers representing the balls to 49 binary digits. Instead of forecasting based on the original six decimal numbers, a machine can predict 49 binary values (either ‘1’ or ‘0’). This approach introduces an additional option beyond the conventional method of directly forecasting using the original decimal numbers.

Lottery Numbers in Bitmap Form

Lottery history data as two-dimensional binary data forms a bitmap. Binary bitmap dataset allows the use of advanced algorithms and machine learning methods developed from 2D image processing fields. For graph service, we provide a 2D bitmap graph for each lottery type, allowing customers to evaluate the visual pattern appearance and distributions.

Viewing the bitmap form data, you can see it resembles a net with knots and black holes. Extending the time axis longer makes it more apparent that the knots and holes are evenly distributed. Even to the human eye, you can sense some number areas are currently in knot zones or hole zones, helping to predict future patterns. We believe future algorithms, deep neural networks, and machine learning models can detect visual patterns better than human eyes.

For more background about data mapping and reflection, refer to our post "Lottery Data From Different Angles".

LSTM Forecasting Methods and Graphs

We provide three types of LSTM forecasting graphs for each lottery type, corresponding to three LSTM forecasting methods:

1. 2D-Multiple-Balls-Together
2. 2D-Individual-Ball-Ranking
3. 1D-Individual-Number

For each type, we provide two graphs: one for the current prediction for the next draw and another for the historical result graph, which plots the past three draws' predictions compared with the actual drawn numbers.

In these history graphs, the past three draws' predictions were recorded and compared with revealed six drawn numbers, the drawn numbers' bar pole is painted orange. It's intuitive for you to evaluate the LSTM model's prediction accuracy.

LSTM Method1: "2D-Multiple-Balls-Together"

The following two graphs are for the "2D-Multiple-Balls-Together" method. In the history graph, you can select "Last Draw," "Second-to-last," or "Third-to-last" for one of the past three draws.

For all three methods, we map the six ball decimal numbers to 49 binary numbers first. The following LSTM data process is on the binary bitmap form. The two "2D" methods set LSTM features to 49, treating the bitmap as a whole (in contrast to the "1D" method below).

The LSTM parameters are set as follows: number_of_features = 49, window_length = 15, epochs = 300, loss = 'mse', optimizer = 'rmsprop'.

Multiple Fit/Predict Loop

Our second unique process, differing from the ordinary LSTM prediction method, is using multiple model.fit/model.predict loops instead of large epoch numbers. From our experiences processing TSRN data, increasing the number of epochs may lead to overfitting, where the model becomes too focused on the training (history) data and performs poorly.

Moreover, for LSTM models, fitting and predicting based on similar preset parameters and datasets may yield different results each time. This is similar to other AI models, where you may not expect the same answer each time for the same question. So, we use a model fit/prediction loop for four times for the "2D-Multiple-Balls-Together" method to accumulate four results of LSTM predictions. Typically, the more model fit/prediction loops, the more helpful the accumulated result.

LSTM Method2: "2D-Individual-Ball-Ranking"

The next two graphs are for the "2D-Individual-Ball-Ranking" method.

The "2D-Individual-Ball-Ranking" method differs from the previous method by processing LSTM predictions for one ball at a time. It maps one ball into a 49-number bitmap instead of six balls. After obtaining the LSTM prediction result counter dataset for each of the six balls, two checksum methods are applied. One method accumulates six counter dataset arrays into one "Sum Count" dataset. The other ranks the top five counters for each ball, setting a rank position dataset array. Then, it accumulates the six balls' rank datasets instead of the prediction counter dataset itself. The "2D-Individual-Ball-Ranking" method also uses model fit/prediction loops, applying LSTM forecasting multiple times.

LSTM Method3: "1D-Individual-Number"

The third method, "1D-Individual-Number," processes the binary data not in bitmap form as a whole but as a 1-dimensional bitstream for each of the 49 numbers. The following graphs illustrate this method result:

Unlike the previous two methods, the "1D-Individual-Number" method treats each of the 49 binary numbers as a separate series. This means the number of features for the LSTM is set to 1 instead of 49, similar to processing 49 threads of Morse code. After predicting each of the 49 numbers individually, we accumulate all the loop result counters to generate the final graph.

Conclusion

You can review and compare all three methods using our intuitive history graphs. These graphs make it easy to evaluate the LSTM models and methods. As you can see, the current techniques cannot guarantee a jackpot win. While they may occasionally exceed the 6/49=0.12245 random chance threshold, the lottery remains a game of chance.

You can sign up and log in to view all other lottery data and LSTM forecasting graphs, including Powerball and Mega Millions from the states, as well as Lotto 649 and Lotto Max from Canada.

If you're serious about exploring new approaches to forecasting time series random numbers, please contact us via email or other means. We have insights and methodologies to discuss.

Good luck with the lottery!

References

(sequence by random)

• "Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras" by Jason Brownlee - https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
• "How to Choose an Activation Function for Deep Learning" by Jason Brownlee - https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning
• "How to Choose an Optimization Algorithm" by Jason Brownlee - https://machinelearningmastery.com/tour-of-optimization-algorithms/
• "How to Guess Accurately 3 Lottery Numbers Out of 6 using LSTM Model" by Roi Polanitzer - https://medium.com/@polanitzer/how-to-guess-accurately-3-lottery-numbers-out-of-6-using-lstm-model-e148d1c632d6
• "How to Guess Accurately 3 Lottery Numbers Out of 6 using LSTM Model" by Roi Polanitzer - https://github.com/TimeSerieser/How-to-Guess-Accurately-3-Lottery-Numbers-Out-of-6-using-LSTM
• "How to Guess Accurately 3 Lottery Numbers Out of 6 using LSTM Model" by
  Satanyu - https://medium.com/@satanyu666/how-to-guess-accurately-3-lottery-numbers-out-of-6-using-lstm-model-44ef080d490c
• "lottery-prediction-lstm" by rahulmod - https://github.com/rahulmod/lottery-prediction-lstm
• "Israeli Lotto Prediction using LSTM" by EM FHAL - https://www.kaggle.com/code/emfhal/israeli-lotto-prediction-using-lstm/notebook
• "EUROMILLION/Lotto Prediction using LSTM" by EM FHAL - https://www.kaggle.com/code/emfhal/euromillion-lotto-prediction-using-lstm
• "Leveraging AI and Python for Lotto Number Prediction" by Huatin OU - https://vocal.media/01/leveraging-ai-and-python-for-lotto-number-prediction

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Question: Why is there no password for member signup and login?

Answer:

The gameseer.net website is built on the Ghost platform, which does not require members to create passwords during signup and login. Instead, it uses a different approach. When someone signs up, they receive an email with a login link (called a magic link). This link grants them access without needing a password. This method simplifies the login process and avoids password-related issues. To log in, members need to check their email, open the message containing the magic link, and click it to gain access.

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Not necessarily. The gameseer.net website inherits the login approach from the Ghost platform but with some improvements. Here’s how it works:

1. When a member signs up, the gameseer.net website records their local IP address and stores it as their home IP address.
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The gameseer.net website offers support for paid members to add or revise their home IP addresses. Currently, this process is handled manually. Paid members can send a support email to contact@gameseer.net, specifying up to 3 IP addresses to be designated as their home IP addresses. Once the support request is received, the gameseer.net staff will manually update the member's profile database with the new IP addresses. This service is provided at no extra charge.

Question: What services does gameseer.net provide?

Answer:

The gameseer.net website offers lottery data visualization graphs for various jackpot lotteries. We are also experimenting with TSRN (Time Series Random Number) forecasting using deep neural networks, specifically LSTM models, utilizing lottery history data as the TSRN dataset.

Question: What types of lotteries are covered by gameseer.net?

Answer:

The gameseer.net website currently provides lottery data graphs for several US and Canadian jackpot lotteries. These include:

• US: Powerball, Mega Millions
• Canada: Lotto 6/49, Lotto Max, Daily Grand, and BC49 More lotteries are planned for inclusion in the future.

Question: What types of lottery data graphs does gameseer.net offer?

Answer:

We offer around 20 types of graphs, categorized into seven groups: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, and Draw Difference.

Each category features its own graph post, which may contain one or multiple graphs. Graphs can be displayed in various styles, such as bar, stacked bar, scatter dot, and more.

Question: What type of lottery data do your graphs display?

Answer:

Typically, we only process regular drawn numbers for lottery history data: "Bonus" numbers, "Powerball" numbers, and "Mega Ball" numbers are not included (except for a few graphs, such as ball history stack-bar graph).

We also only process the main jackpot or main draw. Some lotteries, such as Lotto Max, may have additional drawings that share the same draw number but have different sequence numbers. In such cases, we only process sequence number zero, which is the main draw.

Question: How do you treat the lottery data as "Ball1" to "Ball5," etc.?

Answer:

We sort the regular drawn numbers in ascending order. The smallest drawn number is called "Ball1," the next smallest is "Ball2," and so on. For four lottery types from Canada, the regular drawn numbers are naturally in ascending order. This is not the case for Powerball and Mega Millions from the US.

Question: How quickly are lottery data graphs updated after a lottery draw?

Answer:

Our team updates the graphs immediately after new draw data is released on the official lottery website. The graph posts are typically updated before midnight on the same day. The LSTM forecasting, which requires more resources and time, is usually updated by 8:00 AM the following morning.

Question: Why didn't the graph in the post I browsed update as expected?

Answer:

If a post you often visit has not updated as expected, try closing the post, clearing your browser's history cache, closing the browser, and then reopening it. In most cases, the graph will be updated.

Question: What is a graph preview post, and how does it differ from a regular graph post?

Answer:

A graph preview post provides an overview of all 20 graphs for a specific lottery type through PNG image screenshots. These previews are free to access and allow customers to get a general idea of the available data graphs and their styles.

Unlike static preview images, regular graph posts contain interactive Plotly HTML graphs that offer various data views and selections on different balls, numbers, and draws.

Question: Can I access all the graph and graph preview posts?

Answer:

For BC49, both the graph preview post and all HTML graph posts are free to access.

For other lottery types, you need to sign up as a member to access the HTML graph posts. Registered members can access HTML graph posts for any lottery type, while graph preview posts are free for all users.

Question: I'm not a paid customer and don't want to pay currently. Can I still get the latest data graph and LSTM forecasting graph for the recent draw?

Answer:

Yes. You can freely access all lottery PNG format graphs on the preview posts. These preview posts and their PNG graphs are updated to the latest draw data. The PNG graphs are screenshots; they are not interactive and cannot be zoomed in for investigation.

For BC49, both its graph preview post and HTML graph posts are free to access.

We also suggest you sign up for a few days free access to all posts and HTML interactive graphs.

Question: On what device does the website give me the best experience?

Answer:

The gameseer.net is a cross-platform website. We recommend using a desktop or laptop computer for the best experience. For example, a PC has an Esc key on the keyboard that can quickly toggle between full-screen and embedded graphs.

Large-screen mobile devices also provide a good experience. If you use a small-screen phone, we suggest rotating it for a wider view of the content.

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Answer:

The gameseer.net is powered by the Ghost platform. The website generally works best with modern web standards, so for a smooth experience, it is recommended to use modern iOS versions that support these standards well.

iOS 13 (released in September 2019) and later should provide a reasonable level of compatibility for most features of the Ghost platform and posts.

Question: Does gameseer.net website provide an email newsletter service?

Answer:

While gameseer.net is currently experimenting with email newsletter feeds, it does not offer this service yet. However, the website is actively exploring ways to enhance user engagement and communication..

Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the Canada Lotto 6/49 lottery. Please sign up to access the interactive Lotto 6/49 HTML graph posts listed under the Lotto649 tag, which offer selectable options and much more content. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference

Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the Canada Lotto Max lottery. Please sign up to access the interactive Lotto Max HTML graph posts listed under the LottoMax tag, which offer selectable options and much more content. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference

Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the Canada Daily Grand lottery. Please sign up to access the interactive Daily Grand HTML graph posts listed under the DailyGrand tag, which offer selectable options and much more content. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference

Welcome to GameSeer, your go-to service for visualizing lottery data!

About This Post

This post is a preview introduction, showcasing all 20 graph screenshots for the Canada BC49 lottery. You can visit the interactive BC49 HTML graph posts directly listed under the BC49 tag, which offer selectable options and much more content. They are free to access. You can also sign up to access the interactive HTML graph posts for all other lottery types. Enjoy exploring the data!

Our Services

We offer comprehensive data graphing for various lottery types, including:

• Canada: BC49, Lotto 6/49, Lotto Max, Daily Grand
• US: Powerball, Mega Millions

For each lottery type, we provide 7 graph categories, totaling 20 interactive graphs. Each category features a dedicated post page showcasing the related graphs in Plotly's interactive HTML format. Users can select balls, draws, and numbers, zoom in, and explore many other interactive options.

In addition to these 7 graph category posts, we offer a preview post for each lottery type. These preview posts provide a summary of all graphs in PNG format, offering a quick overview.

Real-Time Updates

Our graphs and preview posts are updated in near real-time, synchronized with the lottery draws, typically within a 4-hour delay.

Access

• BC49: All graph posts are freely accessible to the public.
• Other Lotteries: Graph posts are available exclusively to registered members, except for the preview posts.

Graph Categories

There are 7 categories: Ball Number History, Ball Number Occurrence, Ball Number Occurrence History, Ball Number Range, Ball Number Even/Odd, Ball Difference, Draw Difference.

Category - Ball Number History

Category - Ball Number Occurrence

Category - Ball Number Occurrence History

Category - Ball Number Range

Category - Ball Number Even/Odd

Category - Ball Difference

Category - Draw Difference